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© 2025 Bitly - Handmade in New York City, Denver, Berlin, Bielefeld, and all over the world.

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The Power of Questioning In the Age of Generative AI

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Generative AI - Practical Strategies for Augmented Educators

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Copy of Generative AI - Practical Strategies for Augmented Educators

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GCCTM Winter Workshop - Generative AI As A Math Teaching Partner

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GCCTM Winter Workshop - Generative AI As A Math Teaching Partner

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Char's presentation February 1, 2025 This link will take you to a Youtube video of this presentation https://youtu.be/Y_cKjDcY3BY

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Winter GCCTM Generative AI Math Teaching Partner

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Generative AI - Practical Strategies for Augmented Educators

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Norwalk - Generative AI - Support of Students

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Norwalk - Generative AI - Support of Students

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Generative AI - Classroom Support Of Students

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This is an AI site that responds to your input on style of music, and subject. You can include your own lyrics or let it write the lyrics.

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Create stunning original music for free in seconds using our AI generator. Make your own masterpieces, share with friends, and discover music from artists worldwide.

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Generative AI - Core Subjects Margaretta 

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Generative AI - Core Subjects Margaretta 

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Generative AI - Margaretta  Gr 6-12 Core Subjects

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Generative AI - Int Specialist and Electives 

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Generative AI - Int Specialist and Electives 

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Generative AI - Margaretta  Gr 6-12 Int Specialists and Electives

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This tool was developed in cooperation with the Ohio ESC team, the Dept. of Education and Workforce and AI experts.

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Talk with Claude, an AI assistant from Anthropic

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Char's Alternative Language For Empowering Policy

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Char's Alternative Language For Empowering Policy

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Alternative policy language for generative ai with original language

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AiEDU Generative AI Policy and Best Practice 

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Common Sense Media: Age-Based Media Reviews for Families

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Common Sense Media is the leading source of entertainment and technology recommendations for families. Parents trust our expert reviews and objective advice.

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Microsoft Copilot is your companion to inform, entertain and inspire. Get advice, feedback and straightforward answers. Try Copilot now.

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A Position of the National Council of Teachers of Mathematics

Artificial Intelligence and Mathematics Teaching

NCTM Position

Artificial Intelligence (AI)-driven tools can respond to students' thinking and interests in ways that previous tools could not. By drawing from large language sets, AI has the potential to adjust application-based problems to student interests and identify the sense students have made even in their incorrect answers. Students will continue to need teachers' mathematical, pedagogical, and relational expertise, though teachers are also likely to benefit from AI-driven tools. In some cases, AI may serve as a teaching assistant, but students will need teachers to help them create a bridge between prior knowledge, new knowledge, and shared knowledge. Teachers must tell students to be very skeptical about AI results, especially about the unique challenges of using tools that may have been trained on biased datasets. This skepticism can be woven into existing pedagogical and assessment techniques. Knowing this, educators need to be involved in developing and testing AI tools in math education to stay up to date with current AI trends to best prepare students for an AI future. Contrary to some popular opinions, this effort will require teachers with even deeper knowledge of math instruction and assessment-math teachers with more experience, not less.

Defining Terms

At the time of this writing, advances in AI have come swiftly through the use of Large Language Models (LLM) and Machine Learning (ML) approaches, which build sophisticated statistical predictors by identifying patterns in a massive set of human-curated training data. This is commonly known as generative AI. While this approach is not the only one used in the broader field of AI, our use of the term in this paper is explicitly in reference to technologies derived from generative AI machine learning approaches (e.g., ChatGPT, Bard, etc.).

Historical Context

Mathematics teachers have considered the challenges and advantages of knowledge-generating tools for decades, including calculators, search engines, and apps such as Photomath. In 1980, NCTM stated that calculators helped students develop and use problem-solving skills (NCTM, 1980). In 2015, Webel and Otten stated that "In the conceptual problems, Photomath can assist only with computations; it cannot generalize the patterns" (p. 370). The outcomes of these tools have led teachers to create assessments that reduce computation and increase problem solving. As new answer-generating tools continue to evolve, mathematics teachers need to ensure that their assessments focus students on identifying the reasonableness of the output and the application of the output in specific application-based situations. With technology tools becoming widely available, TODOS (2020) reminds teachers to "ensure systems that maximize equitable access" (p.3) to these tools so that all students January 2024 can benefit from the technologies and the International Society for Technology in Education (ISTE) asks teachers to "consider the capabilities, risks and ethical questions related to using AI."

AI Tools Do Not Replace the Need to Teach Math or Problem Solving

AI tools are only as good as the training input they receive, and conscious or unconscious bias may influence the choice of that training data. This increases the need to teach students to solve problems themselves in order to identify potential bias in the output.

AI is known to "hallucinate" answers that are untrue or unreasonable. Problem-solving and strong fundamentals are essential to develop the intuition for whether an output is reasonable.

AI does not consistently provide accurate citations for the references that it uses, giving users the illusion that the ideas do not need to be cited or vetted. Teachers must teach students how to use the AI output as a stepping point to uncover the primary sources and use those sources to cite their findings.

AI Tools Encourage Teachers to Reimagine Teaching and Assessment

Blending the fundamental computational skills with sensemaking and creative problem solving requires a deep understanding of the math involved, as well as a deep understanding of Pedagogical Content Knowledge (PCK) and Mathematical Knowledge for Teaching (MKT). Educators lacking this expertise can fall into the trap of repetitive, strictly computation "shallow assessments."

AI tools excel at recognizing and solving computational problems (e.g., students who use tools like Photomath when doing homework!). Rather than being threats to math instruction, these tools create positive pressure to avoid the "shallow assessment" trap and create assignments and assessments that blend the fundamentals and creative thinking. This pressure makes the need for experienced math educators more critical-not less.

Even without using AI tools in the classroom, teachers can use the existence of those tools to shift students' thinking from solving alone to a mix of solving and verifying. This requires evolutionary-not revolutionary-change. When the word problems in the book are easily solved by AI tools, the solution isn't to throw out the book; it's to ask students to come up with examples they might use to verify the correctness of a solution generated by those tools. Figuring out how to verify the correctness of multiple potential solutions requires a deeper understanding than coming up with a single solution.

AI Tools Can Personalize Learning

Many teachers are familiar with the need to produce multiple versions of the same test or homework questions, which are focused on the same learning goal but provide different examples of the same underlying math. When developed alongside or used by math educators, AI tools can reduce the labor associated with this task.

Teachers are also familiar with the need to provide engaging, personally relevant problems and questions for every student. Beyond simply "making the math interesting," this is also a critical component of culturally relevant instruction. By dramatically reducing the labor associated with this task, teachers can develop batteries of math problems tailored to students' lived experiences and personal interests.

AI tools can be used to generate multiple explanations for math concepts and formulas aimed at different audiences or levels of expertise. Likewise, they can be used to generate personalized learning experiences that are tailored to a particular audience or level of expertise.

To make these benefits a reality, math educators must be involved in developing and testing AI tools in education. This is especially true given the tendency for AI to introduce or exacerbate existing biases based on patterns in the data on which it was trained.

References

International society for technology in education (2023). Artificial intelligence in education.

https://beta.iste.org/artificial-intelligence-in-education

National Council of Teachers of Mathematics (1980). An agenda for action: Recommendations for school mathematics of the 1980s.

Reston, VA: Author.

TODOS (2020). Equity considerations of access, use, and design of technologies for teaching mathematics [Position statement].

https://www.todos-math.org/statements

Webel, Corey, and Samuel Otten. "Teaching in a World with PhotoMath." Mathematics Teacher 109.5 (2015): 368-373.

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Artificial Intelligence and Mathematics Teaching - National Council of Teachers of Mathematics

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Artificial Intelligence (AI)-driven tools can respond to students' thinking and interests in ways that previous tools could not. By drawing from large language sets, AI has the potential to adjust application-based problems to student interests and identify the sense students have made even in their incorrect answers.

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The Power of Questioning In The Age of Generative AI 

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The Power of Questioning In The Age of Generative AI 

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The Power of Questioning In the Age of Generative AI

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The Power of Questioning In the Age Of Generative AI Char Shryock Education Leadership Consultant North Point ESC CharShryock@gmail.com http://bit.ly/LearnChatGPT

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Find a toolkit for advocating around AI.

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TeachAI brings together education leaders and technology experts to assist governments and education authorities in teaching with and about AI. The initiative is led by Code.org, ETS, the International Society for Technology in Education, Khan Academy, and the World Economic Forum and advised by a diverse group of 100+ organizations, governments, and individuals. TeachAI’s goals include guiding policy, building community and capacity, and increasing awareness. To learn more about TeachAI, please visit www.teachai.org/about.

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Futurepedia - Find The Best AI Tools & Software

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Futurepedia is a free site to help you find the best AI tools and software to make your work and life more efficient and productive. Updated daily, join millions of followers of our website, newsletter and YouTube.

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Practical Guide To Generative Ai For Superintendents

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Practical Guide To Generative Ai For Superintendents

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A Practical Guide To Generative AI For Superintendents

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Title VII of the Civil Rights Act of 1964

Title VII of the Civil Rights Act of 1964 (Title VII) makes it unlawful for an employer to discriminate against someone because of:

Race;

Color;

Religion;

Sex (including pregnancy, childbirth, and related conditions, sexual orientation, and gender identity); or

National origin.

Title VII also makes it unlawful for an employer to take a negative action, or retaliate, against a person because they:

Complained about discrimination, whether formally or informally;

Filed a charge of discrimination with an agency like the U.S. Equal Employment Opportunity Commission, or

Participated as a witness in an employment discrimination investigation or lawsuit.

Title VII also makes it unlawful to use policies or practices that seem neutral but have the effect of discriminating against people because of their race, color, religion, sex (including pregnancy, childbirth, and related conditions, sexual orientation, and gender identity), or national origin.

Under Title VII, it is unlawful to discriminate in any aspect of employment, including:

Hiring and firing;

Compensation, assignment, or classification of workers;

Transfer, promotion, layoff, or recall;

Job advertisements and recruitment;

Testing;

Use of employer facilities;

Training and apprenticeship programs;

Retirement plans, leave, and benefits; or

Other terms and conditions of employment.

Under Title VII, employers also cannot:

Harass an employee because of race, color, religion, sex (including pregnancy, childbirth, and related conditions, sexual orientation, and gender identity), or national origin;

Refuse or fail to make reasonable adjustments to workplace policies or practices that allow individual workers to observe their sincerely held religious beliefs;

Make employment decisions based on stereotypes or assumptions about a person's abilities, traits, or performance because of their race, color, religion, sex (including pregnancy, childbirth, and related conditions, sexual orientation, and gender identity), or national origin;

Deny job opportunities because a person is married to, or associated with, a person of a particular race, color, religion, sex (including pregnancy, childbirth, and related conditions, sexual orientation, and gender identity), or national origin.

The Attorney General, through ELS, brings lawsuits under Title VII against state and local government employers after the EEOC refers a complaint to the Department of Justice. ELS also can start investigations and bring lawsuits against state and local government employers when there is reason to believe that an employer's policy or practice discriminates against a group of job applicants or employees based on their race, color, religion, sex (including pregnancy, childbirth, and related conditions, sexual orientation, and gender identity), or national origin.

Pregnant Workers Fairness Act

The Pregnant Workers Fairness Act (PWFA) is a federal law that covers employers, including state and local government employers, with 15 or more employees. This law requires covered employers to grant "reasonable accommodations" to qualified job applicants and workers with known limitations because of pregnancy, childbirth, or related medical conditions. "Reasonable accommodations" can be changes to how a job is performed or changes to how the workplace normally operates. Some examples of possible reasonable accommodations may include: allowing a correctional officer to have a water bottle or food during their shift, allowing a public school teacher longer or more flexible breaks to use the restroom, providing an emergency medical technician with light duty or help with lifting tasks, temporarily transferring a patrol officer to a less physically demanding or safer position, changing a uniform or dress code to allow maternity clothing, and changing equipment or workspaces, such as providing a state park ranger with a stool to sit on.

The PWFA requires an employer to provide reasonable accommodations unless it would be an undue hardship for the employer. "Undue hardship" means the change would be too difficult or expensive and depends on the facts and the employer. The applicant or employee and the employer must engage in an interactive process before the employer decides how to respond to the worker's request.

The PWFA protects workers who ask for reasonable accommodations, workers who were wrongly denied a reasonable accommodation, and workers who file complaints under the PWFA or who reasonably oppose actions that are illegal under the PWFA. It also protects people from coercion, intimidation, threats, or interference in using their PWFA rights or helping others exercise their PWFA rights.

Under the PWFA, an employer cannot:

Require a qualified worker to accept an accommodation without engaging in an interactive process about the accommodation between the worker and the employer;

Deny a job or other employment opportunity to a qualified employee or applicant because of the person's need for a reasonable accommodation;

Require a qualified employee to take leave, whether paid or unpaid, if another reasonable accommodation would let the person keep working;

Treat a qualified worker worse, also called "taking an adverse action," because the worker asked for or used a reasonable accommodation or opposed unlawful discrimination under the PWFA; or

Interfere with any person's rights under the PWFA.

The Uniformed Services Employment and Reemployment Rights Act of 1994 (USERRA)

The Uniformed Services Employment and Reemployment Rights Act of 1994 (USERRA) ensures that servicemembers can return to their civilian jobs after they finish military service obligations. USERRA requires employers to give servicemembers the seniority, status, and rate of pay that they would have if they had remained continuously employed by their civilian employer without a break for military service.

Employers have other obligations under USERRA, too. For example:

Employers must make reasonable efforts to help returning employees to refresh or upgrade their skills so they can qualify for reemployment;

Returning servicemembers are entitled to immediate reinstatement of health insurance for themselves and previously covered dependents with no waiting period and no exclusion of preexisting conditions other than those that are military service-related; and

Employers must reemploy servicemembers who became disabled during military service in a position most closely related to their former position if they can no longer perform it.

USERRA also prohibits:

Discrimination in hiring, promotion, and retention because of past, present and future military service;

Discrimination in hiring, reemployment, promotion, job retention or benefits because of military service;

Retaliation because a person asserted their rights or assisted (including testifying, giving a statement, etc.) in an USERRA investigation or lawsuit, even if the person assisting has no military service connection.

USERRA covers both voluntary and involuntary military service, in peacetime and wartime, and applies to virtually all civilian employers, including the federal government, state and local governments, and private employers, no matter how many people work for that employer.

Under USERRA, the Department of Justice can bring a lawsuit after the U.S. Department of Labor determines that a servicemember's USERRA rights were violated and refers the complaint to us. The Department of Justice can bring lawsuits under USERRA against private employers as well as state and local government employers.

Executive Order 11246

Executive Order 11246 applies to federal government contractors and federal government-assisted construction contractors and subcontractors who do over $10,000 in federal government business in one year. It prohibits those employers from discriminating based on race, color, religion, sex (including pregnancy, childbirth, and related conditions, sexual orientation, and gender identity) or national origin.

The Executive Order also requires federal government contractors to take action to ensure equal opportunity in all aspects of employment, including training programs, outreach efforts, and other affirmative steps. Contractors must take action to recruit and advance qualified minorities and women for jobs in which they are underused compared to how many people in those groups are available in the workforce. Contractors should include these efforts into their written personnel policies. Federal government contractors with written affirmative action programs must implement them, keep them on file, and update them yearly. Also, under some circumstances, Executive Order 11246 makes it unlawful for federal contractors and subcontractors from taking negative actions against applicants and employees because they have asked about, discuss ed, or shar ed information about their pay or the ir co-workers' pay.

Federal government contractors also must take all necessary actions to make sure no one in the workplace tries to intimidate or discriminate against a person for filing a complaint or participating in a lawsuit under the Executive Order.

Executive Order 11246 is administered by the Office of Federal Contract Compliance Programs at the Department of Labor. After the OFCCP sends a matter to our offices, the Department of Justice can a bring a lawsuit in federal court.

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Title VII of the Civil Rights Act of 1964 (Title VII) makes it unlawful for an employer to discriminate against someone because of: Race; Color; Religion; Sex (including pregnancy, childbirth, and related conditions, sexual orientation, and gender identity); or National origin.

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New on ADA.gov

Explore the new content we've added to ADA.gov

This document discusses and explains the requirements of the ADA's integration mandate and the Supreme Court's decision in Olmstead v. L.C. ex rel. Zimring, 527 U.S. 581 (1999), as applied to segregated employment settings and facility-based day programs. In addition, this document provides a link to the Department's Federal Register notice, which provides more information about this guidance.

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Disability rights are civil rights. From voting to parking, the ADA is a law that protects people with disabilities in many areas of public life.

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Individuals with Disabilities Education Act

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Individuals with Disabilities Education Act (IDEA)

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The U.S. Department of Education’s Individuals with Disabilities Education Act website brings together department and grantee IDEA information and resources. The IDEA makes available a free appropriate public education to and ensures special education and related services to eligible children with disabilities.

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COPPA imposes certain requirements on operators of websites or online services directed to children under 13 years of age, and on operators of other websites or online services that have actual knowledge that they are collecting personal information online from a child under 13 years of age.

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Children's Online Privacy Protection Rule ("COPPA")

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COPPA imposes certain requirements on operators of websites or online services directed to children under 13 years of age, and on operators of other websites or online services that have actual knowledge that they are collecting personal information online from a child under 13 years of age.

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Protecting Student Privacy | U.S. Department of Education

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Student Privacy for... Frequently Asked Questions

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The Ways to Use ChatGPT To Help Students Learn...And Not Cheat

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The Ways to Use ChatGPT To Help Students Learn...And Not Cheat

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Since ChatGPT can engage in conversation and generate essays, computer codes, charts and graphs that closely resemble those created by humans, educators worry students may use it to cheat. A growing number of school districts across the country have decided to block access to ChatGPT on computers and networks.

As professors of educational psychology and educational technology, we've found that the main reason students cheat is their academic motivation. For example, sometimes students are just motivated to get a high grade, whereas other times they are motivated to learn all that they can about a topic.

The decision to cheat or not, therefore, often relates to how academic assignments and tests are constructed and assessed, not on the availability of technological shortcuts. When they have the opportunity to rewrite an essay or retake a test if they don't do well initially, students are less likely to cheat.

We believe teachers can use ChatGPT to increase their students' motivation for learning and actually prevent cheating. Here are three strategies for doing that.

1. Treat ChatGPT as a learning partner

Our research demonstrates that students are more likely to cheat when assignments are designed in ways that encourage them to outperform their classmates. In contrast, students are less likely to cheat when teachers assign academic tasks that prompt them to work collaboratively and to focus on mastering content instead of getting a good grade.

Treating ChatGPT as a learning partner can help teachers shift the focus among their students from competition and performance to collaboration and mastery.

For example, a science teacher can assign students to work with ChatGPT to design a hydroponic vegetable garden. In this scenario, students could engage with ChatGPT to discuss the growing requirements for vegetables, brainstorm design ideas for a hydroponic system and analyze pros and cons of the design.

These activities are designed to promote mastery of content as they focus on the processes of learning rather than just the final grade.

2. Use ChatGPT to boost confidence

Research shows that when students feel confident that they can successfully do the work assigned to them, they are less likely to cheat. And an important way to boost students' confidence is to provide them with opportunities to experience success.

ChatGPT can facilitate such experiences by offering students individualized support and breaking down complex problems into smaller challenges or tasks.

For example, suppose students are asked to attempt to design a hypothetical vehicle that can use gasoline more efficiently than a traditional car. Students who struggle with the project - and might be inclined to cheat - can use ChatGPT to break down the larger problem into smaller tasks. ChatGPT might suggest they first develop an overall concept for the vehicle before determining the size and weight of the vehicle and deciding what type of fuel will be used. Teachers could also ask students to compare the steps suggested by ChatGPT with steps that are recommended by other sources.

3. Prompt ChatGPT to give supportive feedback

It is well documented that personalized feedback supports students' positive emotions, including self-confidence.

ChatGPT can be directed to deliver feedback using positive, empathetic and encouraging language. For example, if a student completes a math problem incorrectly, instead of merely telling the student "You are wrong and the correct answer is ...," ChatGPT may initiate a conversation with the student. Here's a real response generated by ChatGPT: "Your answer is not correct, but it's completely normal to encounter occasional errors or misconceptions along the way. Don't be discouraged by this small setback; you're on the right track! I'm here to support you and answer any questions you may have. You're doing great!"

This will help students feel supported and understood while receiving feedback for improvement. Teachers can easily show students how to direct ChatGPT to provide them such feedback.

We believe that when teachers use ChatGPT and other AI chatbots thoughtfully - and also encourage students to use these tools responsibly in their schoolwork - students have an incentive to learn more and cheat less.

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3 ways to use ChatGPT to help students learn -- and not cheat

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Schools are blocking access to ChatGPT on their computers to try to prevent students from cheating. Two experts on academic cheating offer a very different strategy.

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What Does Authenticity Mean In The Age of Generative AI?  

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What Does Authenticity Mean In The Age of Generative AI?  

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Generative AI thrives on exploiting people's reflexive assumptions of authenticity by producing material that looks like 'the real thing.' artpartner-images/The Image Bank via Getty Images

It turns out that pop stars Drake and The Weeknd didn't suddenly drop a new track that went viral on TikTok and YouTube in April 2023. The photograph that won an international photography competition that same month wasn't a real photograph. And the image of Pope Francis sporting a Balenciaga jacket that appeared in March 2023? That was also a fake.

All were made with the help of generative AI, the new technology that can generate humanlike text, audio and images on demand through programs such as ChatGPT, Midjourney and Bard, among others.

There's certainly something unsettling about the ease with which people can be duped by these fakes, and I see it as a harbinger of an authenticity crisis that raises some difficult questions.

How will voters know whether a video of a political candidate saying something offensive was real or generated by AI? Will people be willing to pay artists for their work when AI can create something visually stunning? Why follow certain authors when stories in their writing style will be freely circulating on the internet?

I've been seeing the anxiety play out all around me at Stanford University, where I'm a professor and also lead a large generative AI and education initiative.

With text, image, audio and video all becoming easier for anyone to produce through new generative AI tools, I believe people are going to need to reexamine and recalibrate how authenticity is judged in the first place.

Fortunately, social science offers some guidance.

The many faces of authenticity

Long before generative AI and ChatGPT rose to the fore, people had been probing what makes something feel authentic.

When a real estate agent is gushing over a property they are trying to sell you, are they being authentic or just trying to close the deal? Is that stylish acquaintance wearing authentic designer fashion or a mass-produced knock-off? As you mature, how do you discover your authentic self?

These are not just philosophical exercises. Neuroscience research has shown that believing a piece of art is authentic will activate the brain's reward centers in ways that viewing something you've been told is a forgery won't.

Authenticity also matters because it is a social glue that reinforces trust. Take the social media misinformation crisis, in which fake news has been inadvertently spread and authentic news decreed fake.

In short, authenticity matters, for both individuals and society as a whole.

But what actually makes something feel authentic?

Psychologist George Newman has explored this question in a series of studies. He found that there are three major dimensions of authenticity.

One of those is historical authenticity, or whether an object is truly from the time, place and person someone claims it to be. An actual painting made by Rembrandt would have historical authenticity; a modern forgery would not.

A second dimension of authenticity is the kind that plays out when, say, a restaurant in Japan offers exceptional and authentic Neapolitan pizza. Their pizza was not made in Naples or imported from Italy. The chef who prepared it may not have a drop of Italian blood in their veins. But the ingredients, appearance and taste may match really well with what tourists would expect to find at a great restaurant in Naples. Newman calls that categorical authenticity.

And finally, there is the authenticity that comes from our values and beliefs. This is the kind that many voters find wanting in politicians and elected leaders who say one thing but do another. It is what admissions officers look for in college essays.

In my own research, I've also seen that authenticity can relate to our expectations about what tools and activities are involved in creating things.

For example, when you see a piece of custom furniture that claims to be handmade, you probably assume that it wasn't literally made by hand - that all sorts of modern tools were nonetheless used to cut, shape and attach each piece. Similarly, if an architect uses computer software to help draw up building plans, you still probably think of the product as legitimate and original. This is because there's a general understanding that those tools are part of what it takes to make those products.

In most of your quick judgments of authenticity, you don't think much about these dimensions. But with generative AI, you will need to.

That's because back when it took a lot of time to produce original new content, there was a general assumption that it required skill to create - that it only could have been made by skilled individuals putting in a lot of effort and acting with the best of intentions.

These are not safe assumptions anymore.

How to deal with the looming authenticity crisis

Generative AI thrives on exploiting people's reliance on categorical authenticity by producing material that looks like "the real thing."

So it'll be important to disentangle historical and categorical authenticity in your own thinking. Just because a recording sounds exactly like Drake - that is, it fits the category expectations for Drake's music - it does not mean that Drake actually recorded it. The great essay that was turned in for a college writing class assignment may not actually be from a student laboring to craft sentences for hours on a word processor.

If it looks like a duck, walks like a duck and quacks like a duck, everyone will need to consider that it may not have actually hatched from an egg.

Also, it'll be important for everyone to get up to speed on what these new generative AI tools really can and can't do. I think this will involve ensuring that people learn about AI in schools and in the workplace, and having open conversations about how creative processes will change with AI being broadly available.

Writing papers for school in the future will not necessarily mean that students have to meticulously form each and every sentence; there are now tools that can help them think of ways to phrase their ideas. And creating an amazing picture won't require exceptional hand-eye coordination or mastery of Adobe Photoshop and Adobe Illustrator.

Finally, in a world where AI operates as a tool, society is going to have to consider how to establish guardrails. These could take the form of regulations, or the creation of norms within certain fields for disclosing how and when AI has been used.

Does AI get credited as a co-author on writing? Is it disallowed on certain types of documents or for certain grade levels in school? Does entering a piece of art into a competition require a signed statement that the artist did not use AI to create their submission? Or does there need to be new, separate competitions that expressly invite AI-generated work?

These questions are tricky. It may be tempting to simply deem generative AI an unacceptable aid, in the same way that calculators are forbidden in some math classes.

However, sequestering new technology risks imposing arbitrary limits on human creative potential. Would the expressive power of images be what it is now if photography had been deemed an unfair use of technology? What if Pixar films were deemed ineligible for the Academy Awards because people thought computer animation tools undermined their authenticity?

The capabilities of generative AI have surprised many and will challenge everyone to think differently. But I believe humans can use AI to expand the boundaries of what is possible and create interesting, worthwhile - and, yes, authentic - works of art, writing and design.

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Generative AI is forcing people to rethink what it means to be authentic

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If it looks like a duck, walks like a duck and quacks like a duck, people now need to pause and wonder whether it actually hatched from an egg.

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Biden Executive Order On Artificial Intelligence 10/30/2023

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Biden Executive Order On Artificial Intelligence 10/30/2023

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By the authority vested in me as President by the Constitution and the laws of the United States of America, it is hereby ordered as follows:

Section 1. Purpose. Artificial intelligence (AI) holds extraordinary potential for both promise and peril. Responsible AI use has the potential to help solve urgent challenges while making our world more prosperous, productive, innovative, and secure. At the same time, irresponsible use could exacerbate societal harms such as fraud, discrimination, bias, and disinformation; displace and disempower workers; stifle competition; and pose risks to national security. Harnessing AI for good and realizing its myriad benefits requires mitigating its substantial risks. This endeavor demands a society-wide effort that includes government, the private sector, academia, and civil society.

My Administration places the highest urgency on governing the development and use of AI safely and responsibly, and is therefore advancing a coordinated, Federal Government-wide approach to doing so. The rapid speed at which AI capabilities are advancing compels the United States to lead in this moment for the sake of our security, economy, and society.

In the end, AI reflects the principles of the people who build it, the people who use it, and the data upon which it is built. I firmly believe that the power of our ideals; the foundations of our society; and the creativity, diversity, and decency of our people are the reasons that America thrived in past eras of rapid change. They are the reasons we will succeed again in this moment. We are more than capable of harnessing AI for justice, security, and opportunity for all.

Sec. 2. Policy and Principles. It is the policy of my Administration to advance and govern the development and use of AI in accordance with eight guiding principles and priorities. When undertaking the actions set forth in this order, executive departments and agencies (agencies) shall, as appropriate and consistent with applicable law, adhere to these principles, while, as feasible, taking into account the views of other agencies, industry, members of academia, civil society, labor unions, international allies and partners, and other relevant organizations:

(a) Artificial Intelligence must be safe and secure. Meeting this goal requires robust, reliable, repeatable, and standardized evaluations of AI systems, as well as policies, institutions, and, as appropriate, other mechanisms to test, understand, and mitigate risks from these systems before they are put to use. It also requires addressing AI systems' most pressing security risks - including with respect to biotechnology, cybersecurity, critical infrastructure, and other national security dangers - while navigating AI's opacity and complexity. Testing and evaluations, including post-deployment performance monitoring, will help ensure that AI systems function as intended, are resilient against misuse or dangerous modifications, are ethically developed and operated in a secure manner, and are compliant with applicable Federal laws and policies. Finally, my Administration will help develop effective labeling and content provenance mechanisms, so that Americans are able to determine when content is generated using AI and when it is not. These actions will provide a vital foundation for an approach that addresses AI's risks without unduly reducing its benefits.

(b) Promoting responsible innovation, competition, and collaboration will allow the United States to lead in AI and unlock the technology's potential to solve some of society's most difficult challenges. This effort requires investments in AI-related education, training, development, research, and capacity, while simultaneously tackling novel intellectual property (IP) questions and other problems to protect inventors and creators. Across the Federal Government, my Administration will support programs to provide Americans the skills they need for the age of AI and attract the world's AI talent to our shores - not just to study, but to stay - so that the companies and technologies of the future are made in America. The Federal Government will promote a fair, open, and competitive ecosystem and marketplace for AI and related technologies so that small developers and entrepreneurs can continue to drive innovation. Doing so requires stopping unlawful collusion and addressing risks from dominant firms' use of key assets such as semiconductors, computing power, cloud storage, and data to disadvantage competitors, and it requires supporting a marketplace that harnesses the benefits of AI to provide new opportunities for small businesses, workers, and entrepreneurs.

(c) The responsible development and use of AI require a commitment to supporting American workers. As AI creates new jobs and industries, all workers need a seat at the table, including through collective bargaining, to ensure that they benefit from these opportunities. My Administration will seek to adapt job training and education to support a diverse workforce and help provide access to opportunities that AI creates. In the workplace itself, AI should not be deployed in ways that undermine rights, worsen job quality, encourage undue worker surveillance, lessen market competition, introduce new health and safety risks, or cause harmful labor-force disruptions. The critical next steps in AI development should be built on the views of workers, labor unions, educators, and employers to support responsible uses of AI that improve workers' lives, positively augment human work, and help all people safely enjoy the gains and opportunities from technological innovation.

(d) Artificial Intelligence policies must be consistent with my Administration's dedication to advancing equity and civil rights. My Administration cannot - and will not - tolerate the use of AI to disadvantage those who are already too often denied equal opportunity and justice. From hiring to housing to healthcare, we have seen what happens when AI use deepens discrimination and bias, rather than improving quality of life. Artificial Intelligence systems deployed irresponsibly have reproduced and intensified existing inequities, caused new types of harmful discrimination, and exacerbated online and physical harms. My Administration will build on the important steps that have already been taken - such as issuing the Blueprint for an AI Bill of Rights, the AI Risk Management Framework, and Executive Order 14091 of February 16, 2023 (Further Advancing Racial Equity and Support for Underserved Communities Through the Federal Government) - in seeking to ensure that AI complies with all Federal laws and to promote robust technical evaluations, careful oversight, engagement with affected communities, and rigorous regulation. It is necessary to hold those developing and deploying AI accountable to standards that protect against unlawful discrimination and abuse, including in the justice system and the Federal Government. Only then can Americans trust AI to advance civil rights, civil liberties, equity, and justice for all.

(e) The interests of Americans who increasingly use, interact with, or purchase AI and AI-enabled products in their daily lives must be protected. Use of new technologies, such as AI, does not excuse organizations from their legal obligations, and hard-won consumer protections are more important than ever in moments of technological change. The Federal Government will enforce existing consumer protection laws and principles and enact appropriate safeguards against fraud, unintended bias, discrimination, infringements on privacy, and other harms from AI. Such protections are especially important in critical fields like healthcare, financial services, education, housing, law, and transportation, where mistakes by or misuse of AI could harm patients, cost consumers or small businesses, or jeopardize safety or rights. At the same time, my Administration will promote responsible uses of AI that protect consumers, raise the quality of goods and services, lower their prices, or expand selection and availability.

(f) Americans' privacy and civil liberties must be protected as AI continues advancing. Artificial Intelligence is making it easier to extract, re-identify, link, infer, and act on sensitive information about people's identities, locations, habits, and desires. Artificial Intelligence's capabilities in these areas can increase the risk that personal data could be exploited and exposed. To combat this risk, the Federal Government will ensure that the collection, use, and retention of data is lawful, is secure, and mitigates privacy and confidentiality risks. Agencies shall use available policy and technical tools, including privacy-enhancing technologies (PETs) where appropriate, to protect privacy and to combat the broader legal and societal risks - including the chilling of First Amendment rights - that result from the improper collection and use of people's data.

(g) It is important to manage the risks from the Federal Government's own use of AI and increase its internal capacity to regulate, govern, and support responsible use of AI to deliver better results for Americans. These efforts start with people, our Nation's greatest asset. My Administration will take steps to attract, retain, and develop public service-oriented AI professionals, including from underserved communities, across disciplines - including technology, policy, managerial, procurement, regulatory, ethical, governance, and legal fields - and ease AI professionals' path into the Federal Government to help harness and govern AI. The Federal Government will work to ensure that all members of its workforce receive adequate training to understand the benefits, risks, and limitations of AI for their job functions, and to modernize Federal Government information technology infrastructure, remove bureaucratic obstacles, and ensure that safe and rights-respecting AI is adopted, deployed, and used.

(h) The Federal Government should lead the way to global societal, economic, and technological progress, as the United States has in previous eras of disruptive innovation and change. This leadership is not measured solely by the technological advancements our country makes. Effective leadership also means pioneering those systems and safeguards needed to deploy technology responsibly - and building and promoting those safeguards with the rest of the world. My Administration will engage with international allies and partners in developing a framework to manage AI's risks, unlock AI's potential for good, and promote common approaches to shared challenges. The Federal Government will seek to promote responsible AI safety and security principles and actions with other nations, including our competitors, while leading key global conversations and collaborations to ensure that AI benefits the whole world, rather than exacerbating inequities, threatening human rights, and causing other harms.

Sec. 3. Definitions. For purposes of this order:

(a) The term "agency" means each agency described in 44 U.S.C. 3502(1), except for the independent regulatory agencies described in 44 U.S.C. 3502(5).

(b) The term "artificial intelligence" or "AI" has the meaning set forth in 15 U.S.C. 9401(3): a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. Artificial intelligence systems use machine- and human-based inputs to perceive real and virtual environments; abstract such perceptions into models through analysis in an automated manner; and use model inference to formulate options for information or action.

(c) The term "AI model" means a component of an information system that implements AI technology and uses computational, statistical, or machine-learning techniques to produce outputs from a given set of inputs.

(d) The term "AI red-teaming" means a structured testing effort to find flaws and vulnerabilities in an AI system, often in a controlled environment and in collaboration with developers of AI. Artificial Intelligence red-teaming is most often performed by dedicated "red teams" that adopt adversarial methods to identify flaws and vulnerabilities, such as harmful or discriminatory outputs from an AI system, unforeseen or undesirable system behaviors, limitations, or potential risks associated with the misuse of the system.

(e) The term "AI system" means any data system, software, hardware, application, tool, or utility that operates in whole or in part using AI.

(f) The term "commercially available information" means any information or data about an individual or group of individuals, including an individual's or group of individuals' device or location, that is made available or obtainable and sold, leased, or licensed to the general public or to governmental or non-governmental entities.

(g) The term "crime forecasting" means the use of analytical techniques to attempt to predict future crimes or crime-related information. It can include machine-generated predictions that use algorithms to analyze large volumes of data, as well as other forecasts that are generated without machines and based on statistics, such as historical crime statistics.

(h) The term "critical and emerging technologies" means those technologies listed in the February 2022 Critical and Emerging Technologies List Update issued by the National Science and Technology Council (NSTC), as amended by subsequent updates to the list issued by the NSTC.

(i) The term "critical infrastructure" has the meaning set forth in section 1016(e) of the USA PATRIOT Act of 2001, 42 U.S.C. 5195c(e).

(j) The term "differential-privacy guarantee" means protections that allow information about a group to be shared while provably limiting the improper access, use, or disclosure of personal information about particular entities.

(k) The term "dual-use foundation model" means an AI model that is trained on broad data; generally uses self-supervision; contains at least tens of billions of parameters; is applicable across a wide range of contexts; and that exhibits, or could be easily modified to exhibit, high levels of performance at tasks that pose a serious risk to security, national economic security, national public health or safety, or any combination of those matters, such as by:

(i) substantially lowering the barrier of entry for non-experts to design, synthesize, acquire, or use chemical, biological, radiological, or nuclear (CBRN) weapons;

(ii) enabling powerful offensive cyber operations through automated vulnerability discovery and exploitation against a wide range of potential targets of cyber attacks; or

(iii) permitting the evasion of human control or oversight through means of deception or obfuscation.

Models meet this definition even if they are provided to end users with technical safeguards that attempt to prevent users from taking advantage of the relevant unsafe capabilities.

(l) The term "Federal law enforcement agency" has the meaning set forth in section 21(a) of Executive Order 14074 of May 25, 2022 (Advancing Effective, Accountable Policing and Criminal Justice Practices To Enhance Public Trust and Public Safety).

(m) The term "floating-point operation" means any mathematical operation or assignment involving floating-point numbers, which are a subset of the real numbers typically represented on computers by an integer of fixed precision scaled by an integer exponent of a fixed base.

(n) The term "foreign person" has the meaning set forth in section 5(c) of Executive Order 13984 of January 19, 2021 (Taking Additional Steps To Address the National Emergency With Respect to Significant Malicious Cyber-Enabled Activities).

(o) The terms "foreign reseller" and "foreign reseller of United States Infrastructure as a Service Products" mean a foreign person who has established an Infrastructure as a Service Account to provide Infrastructure as a Service Products subsequently, in whole or in part, to a third party.

(p) The term "generative AI" means the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content. This can include images, videos, audio, text, and other digital content.

(q) The terms "Infrastructure as a Service Product," "United States Infrastructure as a Service Product," "United States Infrastructure as a Service Provider," and "Infrastructure as a Service Account" each have the respective meanings given to those terms in section 5 of Executive Order 13984.

(r) The term "integer operation" means any mathematical operation or assignment involving only integers, or whole numbers expressed without a decimal point.

(s) The term "Intelligence Community" has the meaning given to that term in section 3.5(h) of Executive Order 12333 of December 4, 1981 (United States Intelligence Activities), as amended.

(t) The term "machine learning" means a set of techniques that can be used to train AI algorithms to improve performance at a task based on data.

(u) The term "model weight" means a numerical parameter within an AI model that helps determine the model's outputs in response to inputs.

(v) The term "national security system" has the meaning set forth in 44 U.S.C. 3552(b)(6).

(w) The term "omics" means biomolecules, including nucleic acids, proteins, and metabolites, that make up a cell or cellular system.

(x) The term "Open RAN" means the Open Radio Access Network approach to telecommunications-network standardization adopted by the O-RAN Alliance, Third Generation Partnership Project, or any similar set of published open standards for multi-vendor network equipment interoperability.

(y) The term "personally identifiable information" has the meaning set forth in Office of Management and Budget (OMB) Circular No. A-130.

(z) The term "privacy-enhancing technology" means any software or hardware solution, technical process, technique, or other technological means of mitigating privacy risks arising from data processing, including by enhancing predictability, manageability, disassociability, storage, security, and confidentiality. These technological means may include secure multiparty computation, homomorphic encryption, zero-knowledge proofs, federated learning, secure enclaves, differential privacy, and synthetic-data-generation tools. This is also sometimes referred to as "privacy-preserving technology."

(aa) The term "privacy impact assessment" has the meaning set forth in OMB Circular No. A-130.

(bb) The term "Sector Risk Management Agency" has the meaning set forth in 6 U.S.C. 650(23).

(cc) The term "self-healing network" means a telecommunications network that automatically diagnoses and addresses network issues to permit self-restoration.

(dd) The term "synthetic biology" means a field of science that involves redesigning organisms, or the biomolecules of organisms, at the genetic level to give them new characteristics. Synthetic nucleic acids are a type of biomolecule redesigned through synthetic-biology methods.

(ee) The term "synthetic content" means information, such as images, videos, audio clips, and text, that has been significantly modified or generated by algorithms, including by AI.

(ff) The term "testbed" means a facility or mechanism equipped for conducting rigorous, transparent, and replicable testing of tools and technologies, including AI and PETs, to help evaluate the functionality, usability, and performance of those tools or technologies.

(gg) The term "watermarking" means the act of embedding information, which is typically difficult to remove, into outputs created by AI - including into outputs such as photos, videos, audio clips, or text - for the purposes of verifying the authenticity of the output or the identity or characteristics of its provenance, modifications, or conveyance.
Sec. 4. Ensuring the Safety and Security of AI Technology.

4.1. Developing Guidelines, Standards, and Best Practices for AI Safety and Security. (a) Within 270 days of the date of this order, to help ensure the development of safe, secure, and trustworthy AI systems, the Secretary of Commerce, acting through the Director of the National Institute of Standards and Technology (NIST), in coordination with the Secretary of Energy, the Secretary of Homeland Security, and the heads of other relevant agencies as the Secretary of Commerce may deem appropriate, shall:

(i) Establish guidelines and best practices, with the aim of promoting consensus industry standards, for developing and deploying safe, secure, and trustworthy AI systems, including:

(A) developing a companion resource to the AI Risk Management Framework, NIST AI 100-1, for generative AI;

(B) developing a companion resource to the Secure Software Development Framework to incorporate secure development practices for generative AI and for dual-use foundation models; and

(C) launching an initiative to create guidance and benchmarks for evaluating and auditing AI capabilities, with a focus on capabilities through which AI could cause harm, such as in the areas of cybersecurity and biosecurity.

(ii) Establish appropriate guidelines (except for AI used as a component of a national security system), including appropriate procedures and processes, to enable developers of AI, especially of dual-use foundation models, to conduct AI red-teaming tests to enable deployment of safe, secure, and trustworthy systems. These efforts shall include:

(A) coordinating or developing guidelines related to assessing and managing the safety, security, and trustworthiness of dual-use foundation models; and

(B) in coordination with the Secretary of Energy and the Director of the National Science Foundation (NSF), developing and helping to ensure the availability of testing environments, such as testbeds, to support the development of safe, secure, and trustworthy AI technologies, as well as to support the design, development, and deployment of associated PETs, consistent with section 9(b) of this order.

(b) Within 270 days of the date of this order, to understand and mitigate AI security risks, the Secretary of Energy, in coordination with the heads of other Sector Risk Management Agencies (SRMAs) as the Secretary of Energy may deem appropriate, shall develop and, to the extent permitted by law and available appropriations, implement a plan for developing the Department of Energy's AI model evaluation tools and AI testbeds. The Secretary shall undertake this work using existing solutions where possible, and shall develop these tools and AI testbeds to be capable of assessing near-term extrapolations of AI systems' capabilities. At a minimum, the Secretary shall develop tools to evaluate AI capabilities to generate outputs that may represent nuclear, nonproliferation, biological, chemical, critical infrastructure, and energy-security threats or hazards. The Secretary shall do this work solely for the purposes of guarding against these threats, and shall also develop model guardrails that reduce such risks. The Secretary shall, as appropriate, consult with private AI laboratories, academia, civil society, and third-party evaluators, and shall use existing solutions.

4.2. Ensuring Safe and Reliable AI. (a) Within 90 days of the date of this order, to ensure and verify the continuous availability of safe, reliable, and effective AI in accordance with the Defense Production Act, as amended, 50 U.S.C. 4501 et seq., including for the national defense and the protection of critical infrastructure, the Secretary of Commerce shall require:

(i) Companies developing or demonstrating an intent to develop potential dual-use foundation models to provide the Federal Government, on an ongoing basis, with information, reports, or records regarding the following:

(A) any ongoing or planned activities related to training, developing, or producing dual-use foundation models, including the physical and cybersecurity protections taken to assure the integrity of that training process against sophisticated threats;

(B) the ownership and possession of the model weights of any dual-use foundation models, and the physical and cybersecurity measures taken to protect those model weights; and

(C) the results of any developed dual-use foundation model's performance in relevant AI red-team testing based on guidance developed by NIST pursuant to subsection 4.1(a)(ii) of this section, and a description of any associated measures the company has taken to meet safety objectives, such as mitigations to improve performance on these red-team tests and strengthen overall model security. Prior to the development of guidance on red-team testing standards by NIST pursuant to subsection 4.1(a)(ii) of this section, this description shall include the results of any red-team testing that the company has conducted relating to lowering the barrier to entry for the development, acquisition, and use of biological weapons by non-state actors; the discovery of software vulnerabilities and development of associated exploits; the use of software or tools to influence real or virtual events; the possibility for self-replication or propagation; and associated measures to meet safety objectives; and

(ii) Companies, individuals, or other organizations or entities that acquire, develop, or possess a potential large-scale computing cluster to report any such acquisition, development, or possession, including the existence and location of these clusters and the amount of total computing power available in each cluster.

(b) The Secretary of Commerce, in consultation with the Secretary of State, the Secretary of Defense, the Secretary of Energy, and the Director of National Intelligence, shall define, and thereafter update as needed on a regular basis, the set of technical conditions for models and computing clusters that would be subject to the reporting requirements of subsection 4.2(a) of this section. Until such technical conditions are defined, the Secretary shall require compliance with these reporting requirements for:

(i) any model that was trained using a quantity of computing power greater than 10 ²⁶ integer or floating-point operations, or using primarily biological sequence data and using a quantity of computing power greater than 10 ²³ integer or floating-point operations; and

(ii) any computing cluster that has a set of machines physically co-located in a single datacenter, transitively connected by data center networking of over 100 Gbit/s, and having a theoretical maximum computing capacity of 10 ²⁰ integer or floating-point operations per second for training AI.

(c) Because I find that additional steps must be taken to deal with the national emergency related to significant malicious cyber-enabled activities declared in Executive Order 13694 of April 1, 2015 (Blocking the Property of Certain Persons Engaging in Significant Malicious Cyber-Enabled Activities), as amended by Executive Order 13757 of December 28, 2016 (Taking Additional Steps to Address the National Emergency With Respect to Significant Malicious Cyber-Enabled Activities), and further amended by Executive Order 13984, to address the use of United States Infrastructure as a Service (IaaS) Products by foreign malicious cyber actors, including to impose additional record-keeping obligations with respect to foreign transactions and to assist in the investigation of transactions involving foreign malicious cyber actors, I hereby direct the Secretary of Commerce, within 90 days of the date of this order, to:

(i) Propose regulations that require United States IaaS Providers to submit a report to the Secretary of Commerce when a foreign person transacts with that United States IaaS Provider to train a large AI model with potential capabilities that could be used in malicious cyber-enabled activity (a "training run"). Such reports shall include, at a minimum, the identity of the foreign person and the existence of any training run of an AI model meeting the criteria set forth in this section, or other criteria defined by the Secretary in regulations, as well as any additional information identified by the Secretary.

(ii) Include a requirement in the regulations proposed pursuant to subsection 4.2(c)(i) of this section that United States IaaS Providers prohibit any foreign reseller of their United States IaaS Product from providing those products unless such foreign reseller submits to the United States IaaS Provider a report, which the United States IaaS Provider must provide to the Secretary of Commerce, detailing each instance in which a foreign person transacts with the foreign reseller to use the United States IaaS Product to conduct a training run described in subsection 4.2(c)(i) of this section. Such reports shall include, at a minimum, the information specified in subsection 4.2(c)(i) of this section as well as any additional information identified by the Secretary.

(iii) Determine the set of technical conditions for a large AI model to have potential capabilities that could be used in malicious cyber-enabled activity, and revise that determination as necessary and appropriate. Until the Secretary makes such a determination, a model shall be considered to have potential capabilities that could be used in malicious cyber-enabled activity if it requires a quantity of computing power greater than 10 ²⁶ integer or floating-point operations and is trained on a computing cluster that has a set of machines physically co-located in a single datacenter, transitively connected by data center networking of over 100 Gbit/s, and having a theoretical maximum compute capacity of 10 ²⁰ integer or floating-point operations per second for training AI.

(d) Within 180 days of the date of this order, pursuant to the finding set forth in subsection 4.2(c) of this section, the Secretary of Commerce shall propose regulations that require United States IaaS Providers to ensure that foreign resellers of United States IaaS Products verify the identity of any foreign person that obtains an IaaS account (account) from the foreign reseller. These regulations shall, at a minimum:

(i) Set forth the minimum standards that a United States IaaS Provider must require of foreign resellers of its United States IaaS Products to verify the identity of a foreign person who opens an account or maintains an existing account with a foreign reseller, including:

(A) the types of documentation and procedures that foreign resellers of United States IaaS Products must require to verify the identity of any foreign person acting as a lessee or sub-lessee of these products or services;

(B) records that foreign resellers of United States IaaS Products must securely maintain regarding a foreign person that obtains an account, including information establishing:

(1) the identity of such foreign person, including name and address;

(2) the means and source of payment (including any associated financial institution and other identifiers such as credit card number, account number, customer identifier, transaction identifiers, or virtual currency wallet or wallet address identifier);

(3) the electronic mail address and telephonic contact information used to verify a foreign person's identity; and

(4) the Internet Protocol addresses used for access or administration and the date and time of each such access or administrative action related to ongoing verification of such foreign person's ownership of such an account; and

(C) methods that foreign resellers of United States IaaS Products must implement to limit all third-party access to the information described in this subsection, except insofar as such access is otherwise consistent with this order and allowed under applicable law;

(ii) Take into consideration the types of accounts maintained by foreign resellers of United States IaaS Products, methods of opening an account, and types of identifying information available to accomplish the objectives of identifying foreign malicious cyber actors using any such products and avoiding the imposition of an undue burden on such resellers; and

(iii) Provide that the Secretary of Commerce, in accordance with such standards and procedures as the Secretary may delineate and in consultation with the Secretary of Defense, the Attorney General, the Secretary of Homeland Security, and the Director of National Intelligence, may exempt a United States IaaS Provider with respect to any specific foreign reseller of their United States IaaS Products, or with respect to any specific type of account or lessee, from the requirements of any regulation issued pursuant to this subsection. Such standards and procedures may include a finding by the Secretary that such foreign reseller, account, or lessee complies with security best practices to otherwise deter abuse of United States IaaS Products.

(e) The Secretary of Commerce is hereby authorized to take such actions, including the promulgation of rules and regulations, and to employ all powers granted to the President by the International Emergency Economic Powers Act, 50 U.S.C. 1701 et seq., as may be necessary to carry out the purposes of subsections 4.2(c) and (d) of this section. Such actions may include a requirement that United States IaaS Providers require foreign resellers of United States IaaS Products to provide United States IaaS Providers verifications relative to those subsections.

4.3. Managing AI in Critical Infrastructure and in Cybersecurity. (a) To ensure the protection of critical
infrastructure, the following actions shall be taken:

(i) Within 90 days of the date of this order, and at least annually thereafter, the head of each agency with relevant regulatory authority over critical infrastructure and the heads of relevant SRMAs, in coordination with the Director of the Cybersecurity and Infrastructure Security Agency within the Department of Homeland Security for consideration of cross-sector risks, shall evaluate and provide to the Secretary of Homeland Security an assessment of potential risks related to the use of AI in critical infrastructure sectors involved, including ways in which deploying AI may make critical infrastructure systems more vulnerable to critical failures, physical attacks, and cyber attacks, and shall consider ways to mitigate these vulnerabilities. Independent regulatory agencies are encouraged, as they deem appropriate, to contribute to sector-specific risk assessments.

(ii) Within 150 days of the date of this order, the Secretary of the Treasury shall issue a public report on best practices for financial institutions to manage AI-specific cybersecurity risks.

(iii) Within 180 days of the date of this order, the Secretary of Homeland Security, in coordination with the Secretary of Commerce and with SRMAs and other regulators as determined by the Secretary of Homeland Security, shall incorporate as appropriate the AI Risk Management Framework, NIST AI 100-1, as well as other appropriate security guidance, into relevant safety and security guidelines for use by critical infrastructure owners and operators.

(iv) Within 240 days of the completion of the guidelines described in subsection 4.3(a)(iii) of this section, the Assistant to the President for National Security Affairs and the Director of OMB, in consultation with the Secretary of Homeland Security, shall coordinate work by the heads of agencies with authority over critical infrastructure to develop and take steps for the Federal Government to mandate such guidelines, or appropriate portions thereof, through regulatory or other appropriate action. Independent regulatory agencies are encouraged, as they deem appropriate, to consider whether to mandate guidance through regulatory action in their areas of authority and responsibility.

(v) The Secretary of Homeland Security shall establish an Artificial Intelligence Safety and Security Board as an advisory committee pursuant to section 871 of the Homeland Security Act of 2002 (Public Law 107-296). The Advisory Committee shall include AI experts from the private sector, academia, and government, as appropriate, and provide to the Secretary of Homeland Security and the Federal Government's critical infrastructure community advice, information, or recommendations for improving security, resilience, and incident response related to AI usage in critical infrastructure.

(b) To capitalize on AI's potential to improve United States cyber defenses:

(i) The Secretary of Defense shall carry out the actions described in subsections 4.3(b)(ii) and (iii) of this section for national security systems, and the Secretary of Homeland Security shall carry out these actions for non-national security systems. Each shall do so in consultation with the heads of other relevant agencies as the Secretary of Defense and the Secretary of Homeland Security may deem appropriate.

(ii) As set forth in subsection 4.3(b)(i) of this section, within 180 days of the date of this order, the Secretary of Defense and the Secretary of Homeland Security shall, consistent with applicable law, each develop plans for, conduct, and complete an operational pilot project to identify, develop, test, evaluate, and deploy AI capabilities, such as large-language models, to aid in the discovery and remediation of vulnerabilities in critical United States Government software, systems, and networks.

(iii) As set forth in subsection 4.3(b)(i) of this section, within 270 days of the date of this order, the Secretary of Defense and the Secretary of Homeland Security shall each provide a report to the Assistant to the President for National Security Affairs on the results of actions taken pursuant to the plans and operational pilot projects required by subsection 4.3(b)(ii) of this section, including a description of any vulnerabilities found and fixed through the development and deployment of AI capabilities and any lessons learned on how to identify, develop, test, evaluate, and deploy AI capabilities effectively for cyber defense.

4.4. Reducing Risks at the Intersection of AI and CBRN Threats. (a) To better understand and mitigate the risk of AI being misused to assist in the development or use of CBRN threats - with a particular focus on biological weapons - the following actions shall be taken:

(i) Within 180 days of the date of this order, the Secretary of Homeland Security, in consultation with the Secretary of Energy and the Director of the Office of Science and Technology Policy (OSTP), shall evaluate the potential for AI to be misused to enable the development or production of CBRN threats, while also considering the benefits and application of AI to counter these threats, including, as appropriate, the results of work conducted under section 8(b) of this order. The Secretary of Homeland Security shall:

(A) consult with experts in AI and CBRN issues from the Department of Energy, private AI laboratories, academia, and third-party model evaluators, as appropriate, to evaluate AI model capabilities to present CBRN threats - for the sole purpose of guarding against those threats - as well as options for minimizing the risks of AI model misuse to generate or exacerbate those threats; and

(B) submit a report to the President that describes the progress of these efforts, including an assessment of the types of AI models that may present CBRN risks to the United States, and that makes recommendations for regulating or overseeing the training, deployment, publication, or use of these models, including requirements for safety evaluations and guardrails for mitigating potential threats to national security.

(ii) Within 120 days of the date of this order, the Secretary of Defense, in consultation with the Assistant to the President for National Security Affairs and the Director of OSTP, shall enter into a contract with the National Academies of Sciences, Engineering, and Medicine to conduct - and submit to the Secretary of Defense, the Assistant to the President for National Security Affairs, the Director of the Office of Pandemic Preparedness and Response Policy, the Director of OSTP, and the Chair of the Chief Data Officer Council - a study that:

(A) assesses the ways in which AI can increase biosecurity risks, including risks from generative AI models trained on biological data, and makes recommendations on how to mitigate these risks;

(B) considers the national security implications of the use of data and datasets, especially those associated with pathogens and omics studies, that the United States Government hosts, generates, funds the creation of, or otherwise owns, for the training of generative AI models, and makes recommendations on how to mitigate the risks related to the use of these data and datasets;

(C) assesses the ways in which AI applied to biology can be used to reduce biosecurity risks, including recommendations on opportunities to coordinate data and high-performance computing resources; and

(D) considers additional concerns and opportunities at the intersection of AI and synthetic biology that the Secretary of Defense deems appropriate.

(b) To reduce the risk of misuse of synthetic nucleic acids, which could be substantially increased by AI's capabilities in this area, and improve biosecurity measures for the nucleic acid synthesis industry, the following actions shall be taken:

(i) Within 180 days of the date of this order, the Director of OSTP, in consultation with the Secretary of State, the Secretary of Defense, the Attorney General, the Secretary of Commerce, the Secretary of Health and Human Services (HHS), the Secretary of Energy, the Secretary of Homeland Security, the Director of National Intelligence, and the heads of other relevant agencies as the Director of OSTP may deem appropriate, shall establish a framework, incorporating, as appropriate, existing United States Government guidance, to encourage providers of synthetic nucleic acid sequences to implement comprehensive, scalable, and verifiable synthetic nucleic acid procurement screening mechanisms, including standards and recommended incentives. As part of this framework, the Director of OSTP shall:

(A) establish criteria and mechanisms for ongoing identification of biological sequences that could be used in a manner that would pose a risk to the national security of the United States; and

(B) determine standardized methodologies and tools for conducting and verifying the performance of sequence synthesis procurement screening, including customer screening approaches to support due diligence with respect to managing security risks posed by purchasers of biological sequences identified in subsection 4.4(b)(i)(A) of this section, and processes for the reporting of concerning activity to enforcement entities.

(ii) Within 180 days of the date of this order, the Secretary of Commerce, acting through the Director of NIST, in coordination with the Director of OSTP, and in consultation with the Secretary of State, the Secretary of HHS, and the heads of other relevant agencies as the Secretary of Commerce may deem appropriate, shall initiate an effort to engage with industry and relevant stakeholders, informed by the framework developed under subsection 4.4(b)(i) of this section, to develop and refine for possible use by synthetic nucleic acid sequence providers:

(A) specifications for effective nucleic acid synthesis procurement screening;

(B) best practices, including security and access controls, for managing sequence-of-concern databases to support such screening;

(C) technical implementation guides for effective screening; and

(D) conformity-assessment best practices and mechanisms.

(iii) Within 180 days of the establishment of the framework pursuant to subsection 4.4(b)(i) of this section, all agencies that fund life-sciences research shall, as appropriate and consistent with applicable law, establish that, as a requirement of funding, synthetic nucleic acid procurement is conducted through providers or manufacturers that adhere to the framework, such as through an attestation from the provider or manufacturer. The Assistant to the President for National Security Affairs and the Director of OSTP shall coordinate the process of reviewing such funding requirements to facilitate consistency in implementation of the framework across funding agencies.

(iv) In order to facilitate effective implementation of the measures described in subsections 4.4(b)(i)-(iii) of this section, the Secretary of Homeland Security, in consultation with the heads of other relevant agencies as the Secretary of Homeland Security may deem appropriate, shall:

(A) within 180 days of the establishment of the framework pursuant to subsection 4.4(b)(i) of this section, develop a framework to conduct structured evaluation and stress testing of nucleic acid synthesis procurement screening, including the systems developed in accordance with subsections 4.4(b)(i)-(ii) of this section and implemented by providers of synthetic nucleic acid sequences; and

(B) following development of the framework pursuant to subsection 4.4(b)(iv)(A) of this section, submit an annual report to the Assistant to the President for National Security Affairs, the Director of the Office of Pandemic Preparedness and Response Policy, and the Director of OSTP on any results of the activities conducted pursuant to subsection 4.4(b)(iv)(A) of this section, including recommendations, if any, on how to strengthen nucleic acid synthesis procurement screening, including customer screening systems.

4.5. Reducing the Risks Posed by Synthetic Content.

To foster capabilities for identifying and labeling synthetic content produced by AI systems, and to establish the authenticity and provenance of digital content, both synthetic and not synthetic, produced by the Federal Government or on its behalf:

(a) Within 240 days of the date of this order, the Secretary of Commerce, in consultation with the heads of other relevant agencies as the Secretary of Commerce may deem appropriate, shall submit a report to the Director of OMB and the Assistant to the President for National Security Affairs identifying the existing standards, tools, methods, and practices, as well as the potential development of further science-backed standards and techniques, for:

(i) authenticating content and tracking its provenance;

(ii) labeling synthetic content, such as using watermarking;

(iii) detecting synthetic content;

(iv) preventing generative AI from producing child sexual abuse material or producing non-consensual intimate imagery of real individuals (to include intimate digital depictions of the body or body parts of an identifiable individual);

(v) testing software used for the above purposes; and

(vi) auditing and maintaining synthetic content.

(b) Within 180 days of submitting the report required under subsection 4.5(a) of this section, and updated periodically thereafter, the Secretary of Commerce, in coordination with the Director of OMB, shall develop guidance regarding the existing tools and practices for digital content authentication and synthetic content detection measures. The guidance shall include measures for the purposes listed in subsection 4.5(a) of this section.

(c) Within 180 days of the development of the guidance required under subsection 4.5(b) of this section, and updated periodically thereafter, the Director of OMB, in consultation with the Secretary of State; the Secretary of Defense; the Attorney General; the Secretary of Commerce, acting through the Director of NIST; the Secretary of Homeland Security; the Director of National Intelligence; and the heads of other agencies that the Director of OMB deems appropriate, shall - for the purpose of strengthening public confidence in the integrity of official United States Government digital content - issue guidance to agencies for labeling and authenticating such content that they produce or publish.

(d) The Federal Acquisition Regulatory Council shall, as appropriate and consistent with applicable law, consider amending the Federal Acquisition Regulation to take into account the guidance established under subsection 4.5 of this section.

4.6. Soliciting Input on Dual-Use Foundation Models with Widely Available Model Weights. When the weights for a dual-use foundation model are widely available - such as when they are publicly posted on the Internet - there can be substantial benefits to innovation, but also substantial security risks, such as the removal of safeguards within the model. To address the risks and potential benefits of dual-use foundation models with widely available weights, within 270 days of the date of this order, the Secretary of Commerce, acting through the Assistant Secretary of Commerce for Communications and Information, and in consultation with the Secretary of State, shall:

(a) solicit input from the private sector, academia, civil society, and other stakeholders through a public consultation process on potential risks, benefits, other implications, and appropriate policy and regulatory approaches related to dual-use foundation models for which the model weights are widely available, including:

(i) risks associated with actors fine-tuning dual-use foundation models for which the model weights are widely available or removing those models' safeguards;

(ii) benefits to AI innovation and research, including research into AI safety and risk management, of dual-use foundation models for which the model weights are widely available; and

(iii) potential voluntary, regulatory, and international mechanisms to manage the risks and maximize the benefits of dual-use foundation models for which the model weights are widely available; and

(b) based on input from the process described in subsection 4.6(a) of this section, and in consultation with the heads of other relevant agencies as the Secretary of Commerce deems appropriate, submit a report to the President on the potential benefits, risks, and implications of dual-use foundation models for which the model weights are widely available, as well as policy and regulatory recommendations pertaining to those models.

4.7. Promoting Safe Release and Preventing the Malicious Use of Federal Data for AI Training.To improve public data access and manage security risks, and consistent with the objectives of the Open, Public, Electronic, and Necessary Government Data Act (title II of Public Law 115-435) to expand public access to Federal data assets in a machine-readable format while also taking into account security considerations, including the risk that information in an individual data asset in isolation does not pose a security risk but, when combined with other available information, may pose such a risk:

(a) within 270 days of the date of this order, the Chief Data Officer Council, in consultation with the Secretary of Defense, the Secretary of Commerce, the Secretary of Energy, the Secretary of Homeland Security, and the Director of National Intelligence, shall develop initial guidelines for performing security reviews, including reviews to identify and manage the potential security risks of releasing Federal data that could aid in the development of CBRN weapons as well as the development of autonomous offensive cyber capabilities, while also providing public access to Federal Government data in line with the goals stated in the Open, Public, Electronic, and Necessary Government Data Act (title II of Public Law 115-435); and

(b) within 180 days of the development of the initial guidelines required by subsection 4.7(a) of this section, agencies shall conduct a security review of all data assets in the comprehensive data inventory required under 44 U.S.C. 3511(a)(1) and (2)(B) and shall take steps, as appropriate and consistent with applicable law, to address the highest-priority potential security risks that releasing that data could raise with respect to CBRN weapons, such as the ways in which that data could be used to train AI systems.

4.8. Directing the Development of a National Security Memorandum. To develop a coordinated executive branch approach to managing AI's security risks, the Assistant to the President for National Security Affairs and the Assistant to the President and Deputy Chief of Staff for Policy shall oversee an interagency process with the purpose of, within 270 days of the date of this order, developing and submitting a proposed National Security Memorandum on AI to the President. The memorandum shall address the governance of AI used as a component of a national security system or for military and intelligence purposes. The memorandum shall take into account current efforts to govern the development and use of AI for national security systems. The memorandum shall outline actions for the Department of Defense, the Department of State, other relevant agencies, and the Intelligence Community to address the national security risks and potential benefits posed by AI. In particular, the memorandum shall:

(a) provide guidance to the Department of Defense, other relevant agencies, and the Intelligence Community on the continued adoption of AI capabilities to advance the United States national security mission, including through directing specific AI assurance and risk-management practices for national security uses of AI that may affect the rights or safety of United States persons and, in appropriate contexts, non-United States persons; and

(b) direct continued actions, as appropriate and consistent with applicable law, to address the potential use of AI systems by adversaries and other foreign actors in ways that threaten the capabilities or objectives of the Department of Defense or the Intelligence Community, or that otherwise pose risks to the security of the United States or its allies and partners.

Sec. 5. Promoting Innovation and Competition.

5.1. Attracting AI Talent to the United States. (a) Within 90 days of the date of this order, to attract and retain talent in AI and other critical and emerging technologies in the United States economy, the Secretary of State and the Secretary of Homeland Security shall take appropriate steps to:

(i) streamline processing times of visa petitions and applications, including by ensuring timely availability of visa appointments, for noncitizens who seek to travel to the United States to work on, study, or conduct research in AI or other critical and emerging technologies; and

(ii) facilitate continued availability of visa appointments in sufficient volume for applicants with expertise in AI or other critical and emerging technologies.

(b) Within 120 days of the date of this order, the Secretary of State shall:

(i) consider initiating a rulemaking to establish new criteria to designate countries and skills on the Department of State's Exchange Visitor Skills List as it relates to the 2-year foreign residence requirement for certain J-1 nonimmigrants, including those skills that are critical to the United States;

(ii) consider publishing updates to the 2009 Revised Exchange Visitor Skills List (74 FR 20108); and

(iii) consider implementing a domestic visa renewal program under 22 C.F.R. 41.111(b) to facilitate the ability of qualified applicants, including highly skilled talent in AI and critical and emerging technologies, to continue their work in the United States without unnecessary interruption.

(c) Within 180 days of the date of this order, the Secretary of State shall:

(i) consider initiating a rulemaking to expand the categories of nonimmigrants who qualify for the domestic visa renewal program covered under 22 C.F.R. 41.111(b) to include academic J-1 research scholars and F-1 students in science, technology, engineering, and mathematics (STEM); and

(ii) establish, to the extent permitted by law and available appropriations, a program to identify and attract top talent in AI and other critical and emerging technologies at universities, research institutions, and the private sector overseas, and to establish and increase connections with that talent to educate them on opportunities and resources for research and employment in the United States, including overseas educational components to inform top STEM talent of nonimmigrant and immigrant visa options and potential expedited adjudication of their visa petitions and applications.

(d) Within 180 days of the date of this order, the Secretary of Homeland Security shall:

(i) review and initiate any policy changes the Secretary determines necessary and appropriate to clarify and modernize immigration pathways for experts in AI and other critical and emerging technologies, including O-1A and EB-1 noncitizens of extraordinary ability; EB-2 advanced-degree holders and noncitizens of exceptional ability; and startup founders in AI and other critical and emerging technologies using the International Entrepreneur Rule; and

(ii) continue its rulemaking process to modernize the H-1B program and enhance its integrity and usage, including by experts in AI and other critical and emerging technologies, and consider initiating a rulemaking to enhance the process for noncitizens, including experts in AI and other critical and emerging technologies and their spouses, dependents, and children, to adjust their status to lawful permanent resident.

(e) Within 45 days of the date of this order, for purposes of considering updates to the "Schedule A" list of occupations, 20 C.F.R. 656.5, the Secretary of Labor shall publish a request for information (RFI) to solicit public input, including from industry and worker-advocate communities, identifying AI and other STEM-related occupations, as well as additional occupations across the economy, for which there is an insufficient number of ready, willing, able, and qualified United States workers.

(f) The Secretary of State and the Secretary of Homeland Security shall, consistent with applicable law and implementing regulations, use their discretionary authorities to support and attract foreign nationals with special skills in AI and other critical and emerging technologies seeking to work, study, or conduct research in the United States.

(g) Within 120 days of the date of this order, the Secretary of Homeland Security, in consultation with the Secretary of State, the Secretary of Commerce, and the Director of OSTP, shall develop and publish informational resources to better attract and retain experts in AI and other critical and emerging technologies, including:

(i) a clear and comprehensive guide for experts in AI and other critical and emerging technologies to understand their options for working in the United States, to be published in multiple relevant languages on AI.gov; and

(ii) a public report with relevant data on applications, petitions, approvals, and other key indicators of how experts in AI and other critical and emerging technologies have utilized the immigration system through the end of Fiscal Year 2023.

5.2. Promoting Innovation. (a) To develop and strengthen public-private partnerships for advancing innovation, commercialization, and risk-mitigation methods for AI, and to help promote safe, responsible, fair, privacy-protecting, and trustworthy AI systems, the Director of NSF shall take the following steps:

(i) Within 90 days of the date of this order, in coordination with the heads of agencies that the Director of NSF deems appropriate, launch a pilot program implementing the National AI Research Resource (NAIRR), consistent with past recommendations of the NAIRR Task Force. The program shall pursue the infrastructure, governance mechanisms, and user interfaces to pilot an initial integration of distributed computational, data, model, and training resources to be made available to the research community in support of AI-related research and development. The Director of NSF shall identify Federal and private sector computational, data, software, and training resources appropriate for inclusion in the NAIRR pilot program. To assist with such work, within 45 days of the date of this order, the heads of agencies whom the Director of NSF identifies for coordination pursuant to this subsection shall each submit to the Director of NSF a report identifying the agency resources that could be developed and integrated into such a pilot program. These reports shall include a description of such resources, including their current status and availability; their format, structure, or technical specifications; associated agency expertise that will be provided; and the benefits and risks associated with their inclusion in the NAIRR pilot program. The heads of independent regulatory agencies are encouraged to take similar steps, as they deem appropriate.

(ii) Within 150 days of the date of this order, fund and launch at least one NSF Regional Innovation Engine that prioritizes AI-related work, such as AI-related research, societal, or workforce needs.

(iii) Within 540 days of the date of this order, establish at least four new National AI Research Institutes, in addition to the 25 currently funded as of the date of this order.

(b) Within 120 days of the date of this order, to support activities involving high-performance and data-intensive computing, the Secretary of Energy, in coordination with the Director of NSF, shall, in a manner consistent with applicable law and available appropriations, establish a pilot program to enhance existing successful training programs for scientists, with the goal of training 500 new researchers by 2025 capable of meeting the rising demand for AI talent.

(c) To promote innovation and clarify issues related to AI and inventorship of patentable subject matter, the Under Secretary of Commerce for Intellectual Property and Director of the United States Patent and Trademark Office (USPTO Director) shall:

(i) within 120 days of the date of this order, publish guidance to USPTO patent examiners and applicants addressing inventorship and the use of AI, including generative AI, in the inventive process, including illustrative examples in which AI systems play different roles in inventive processes and how, in each example, inventorship issues ought to be analyzed;

(ii) subsequently, within 270 days of the date of this order, issue additional guidance to USPTO patent examiners and applicants to address other considerations at the intersection of AI and IP, which could include, as the USPTO Director deems necessary, updated guidance on patent eligibility to address innovation in AI and critical and emerging technologies; and

(iii) within 270 days of the date of this order or 180 days after the United States Copyright Office of the Library of Congress publishes its forthcoming AI study that will address copyright issues raised by AI, whichever comes later, consult with the Director of the United States Copyright Office and issue recommendations to the President on potential executive actions relating to copyright and AI. The recommendations shall address any copyright and related issues discussed in the United States Copyright Office's study, including the scope of protection for works produced using AI and the treatment of copyrighted works in AI training.

(d) Within 180 days of the date of this order, to assist developers of AI in combatting AI-related IP risks, the Secretary of Homeland Security, acting through the Director of the National Intellectual Property Rights Coordination Center, and in consultation with the Attorney General, shall develop a training, analysis, and evaluation program to mitigate AI-related IP risks. Such a program shall:

(i) include appropriate personnel dedicated to collecting and analyzing reports of AI-related IP theft, investigating such incidents with implications for national security, and, where appropriate and consistent with applicable law, pursuing related enforcement actions;

(ii) implement a policy of sharing information and coordinating on such work, as appropriate and consistent with applicable law, with the Federal Bureau of Investigation; United States Customs and Border Protection; other agencies; State and local agencies; and appropriate international organizations, including through work-sharing agreements;

(iii) develop guidance and other appropriate resources to assist private sector actors with mitigating the risks of AI-related IP theft;

(iv) share information and best practices with AI developers and law enforcement personnel to identify incidents, inform stakeholders of current legal requirements, and evaluate AI systems for IP law violations, as well as develop mitigation strategies and resources; and

(v) assist the Intellectual Property Enforcement Coordinator in updating the Intellectual Property Enforcement Coordinator Joint Strategic Plan on Intellectual Property Enforcement to address AI-related issues.

(e) To advance responsible AI innovation by a wide range of healthcare technology developers that promotes the welfare of patients and workers in the healthcare sector, the Secretary of HHS shall identify and, as appropriate and consistent with applicable law and the activities directed in section 8 of this order, prioritize grantmaking and other awards, as well as undertake related efforts, to support responsible AI development and use, including:

(i) collaborating with appropriate private sector actors through HHS programs that may support the advancement of AI-enabled tools that develop personalized immune-response profiles for patients, consistent with section 4 of this order;

(ii) prioritizing the allocation of 2024 Leading Edge Acceleration Project cooperative agreement awards to initiatives that explore ways to improve healthcare-data quality to support the responsible development of AI tools for clinical care, real-world-evidence programs, population health, public health, and related research; and

(iii) accelerating grants awarded through the National Institutes of Health Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program and showcasing current AIM-AHEAD activities in underserved communities.

(f) To advance the development of AI systems that improve the quality of veterans' healthcare, and in order to support small businesses' innovative capacity, the Secretary of Veterans Affairs shall:

(i) within 365 days of the date of this order, host two 3-month nationwide AI Tech Sprint competitions; and

(ii) as part of the AI Tech Sprint competitions and in collaboration with appropriate partners, provide participants access to technical assistance, mentorship opportunities, individualized expert feedback on products under development, potential contract opportunities, and other programming and resources.

(g) Within 180 days of the date of this order, to support the goal of strengthening our Nation's resilience against climate change impacts and building an equitable clean energy economy for the future, the Secretary of Energy, in consultation with the Chair of the Federal Energy Regulatory Commission, the Director of OSTP, the Chair of the Council on Environmental Quality, the Assistant to the President and National Climate Advisor, and the heads of other relevant agencies as the Secretary of Energy may deem appropriate, shall:

(i) issue a public report describing the potential for AI to improve planning, permitting, investment, and operations for electric grid infrastructure and to enable the provision of clean, affordable, reliable, resilient, and secure electric power to all Americans;

(ii) develop tools that facilitate building foundation models useful for basic and applied science, including models that streamline permitting and environmental reviews while improving environmental and social outcomes;

(iii) collaborate, as appropriate, with private sector organizations and members of academia to support development of AI tools to mitigate climate change risks;

(iv) take steps to expand partnerships with industry, academia, other agencies, and international allies and partners to utilize the Department of Energy's computing capabilities and AI testbeds to build foundation models that support new applications in science and energy, and for national security, including partnerships that increase community preparedness for climate-related risks, enable clean-energy deployment (including addressing delays in permitting reviews), and enhance grid reliability and resilience; and

(v) establish an office to coordinate development of AI and other critical and emerging technologies across Department of Energy programs and the 17 National Laboratories.

(h) Within 180 days of the date of this order, to understand AI's implications for scientific research, the President's Council of Advisors on Science and Technology shall submit to the President and make publicly available a report on the potential role of AI, especially given recent developments in AI, in research aimed at tackling major societal and global challenges. The report shall include a discussion of issues that may hinder the effective use of AI in research and practices needed to ensure that AI is used responsibly for research.

5.3. Promoting Competition. (a) The head of each agency developing policies and regulations related to AI shall use their authorities, as appropriate and consistent with applicable law, to promote competition in AI and related technologies, as well as in other markets. Such actions include addressing risks arising from concentrated control of key inputs, taking steps to stop unlawful collusion and prevent dominant firms from disadvantaging competitors, and working to provide new opportunities for small businesses and entrepreneurs. In particular, the Federal Trade Commission is encouraged to consider, as it deems appropriate, whether to exercise the Commission's existing authorities, including its rulemaking authority under the Federal Trade Commission Act, 15 U.S.C. 41 et seq., to ensure fair competition in the AI marketplace and to ensure that consumers and workers are protected from harms that may be enabled by the use of AI.

(b) To promote competition and innovation in the semiconductor industry, recognizing that semiconductors power AI technologies and that their availability is critical to AI competition, the Secretary of Commerce shall, in implementing division A of Public Law 117-167, known as the Creating Helpful Incentives to Produce Semiconductors (CHIPS) Act of 2022, promote competition by:

(i) implementing a flexible membership structure for the National Semiconductor Technology Center that attracts all parts of the semiconductor and microelectronics ecosystem, including startups and small firms;

(ii) implementing mentorship programs to increase interest and participation in the semiconductor industry, including from workers in underserved communities;

(iii) increasing, where appropriate and to the extent permitted by law, the availability of resources to startups and small businesses, including:

(A) funding for physical assets, such as specialty equipment or facilities, to which startups and small businesses may not otherwise have access;

(B) datasets - potentially including test and performance data - collected, aggregated, or shared by CHIPS research and development programs;

(C) workforce development programs;

(D) design and process technology, as well as IP, as appropriate; and

(E) other resources, including technical and intellectual property assistance, that could accelerate commercialization of new technologies by startups and small businesses, as appropriate; and

(iv) considering the inclusion, to the maximum extent possible, and as consistent with applicable law, of competition-increasing measures in notices of funding availability for commercial research-and-development facilities focused on semiconductors, including measures that increase access to facility capacity for startups or small firms developing semiconductors used to power AI technologies.

(c) To support small businesses innovating and commercializing AI, as well as in responsibly adopting and deploying AI, the Administrator of the Small Business Administration shall:

(i) prioritize the allocation of Regional Innovation Cluster program funding for clusters that support planning activities related to the establishment of one or more Small Business AI Innovation and Commercialization Institutes that provide support, technical assistance, and other resources to small businesses seeking to innovate, commercialize, scale, or otherwise advance the development of AI;

(ii) prioritize the allocation of up to $2 million in Growth Accelerator Fund Competition bonus prize funds for accelerators that support the incorporation or expansion of AI-related curricula, training, and technical assistance, or other AI-related resources within their programming; and

(iii) assess the extent to which the eligibility criteria of existing programs, including the State Trade Expansion Program, Technical and Business Assistance funding, and capital-access programs - such as the 7(a) loan program, 504 loan program, and Small Business Investment Company (SBIC) program - support appropriate expenses by small businesses related to the adoption of AI and, if feasible and appropriate, revise eligibility criteria to improve support for these expenses.

(d) The Administrator of the Small Business Administration, in coordination with resource partners, shall conduct outreach regarding, and raise awareness of, opportunities for small businesses to use capital-access programs described in subsection 5.3(c) of this section for eligible AI-related purposes, and for eligible investment funds with AI-related expertise - particularly those seeking to serve or with experience serving underserved communities - to apply for an SBIC license.

Sec. 6. Supporting Workers.(a) To advance the Government's understanding of AI's implications for workers, the following actions shall be taken within 180 days of the date of this order:

(i) The Chairman of the Council of Economic Advisers shall prepare and submit a report to the President on the labor-market effects of AI.

(ii) To evaluate necessary steps for the Federal Government to address AI-related workforce disruptions, the Secretary of Labor shall submit to the President a report analyzing the abilities of agencies to support workers displaced by the adoption of AI and other technological advancements. The report shall, at a minimum:

(A) assess how current or formerly operational Federal programs designed to assist workers facing job disruptions - including unemployment insurance and programs authorized by the Workforce Innovation and Opportunity Act (Public Law 113-128) - could be used to respond to possible future AI-related disruptions; and

(B) identify options, including potential legislative measures, to strengthen or develop additional Federal support for workers displaced by AI and, in consultation with the Secretary of Commerce and the Secretary of Education, strengthen and expand education and training opportunities that provide individuals pathways to occupations related to AI.

(b) To help ensure that AI deployed in the workplace advances employees' well-being:

(i) The Secretary of Labor shall, within 180 days of the date of this order and in consultation with other agencies and with outside entities, including labor unions and workers, as the Secretary of Labor deems appropriate, develop and publish principles and best practices for employers that could be used to mitigate AI's potential harms to employees' well-being and maximize its potential benefits. The principles and best practices shall include specific steps for employers to take with regard to AI, and shall cover, at a minimum:

(A) job-displacement risks and career opportunities related to AI, including effects on job skills and evaluation of applicants and workers;

(B) labor standards and job quality, including issues related to the equity, protected-activity, compensation, health, and safety implications of AI in the workplace; and

(C) implications for workers of employers' AI-related collection and use of data about them, including transparency, engagement, management, and activity protected under worker-protection laws.

(ii) After principles and best practices are developed pursuant to subsection (b)(i) of this section, the heads of agencies shall consider, in consultation with the Secretary of Labor, encouraging the adoption of these guidelines in their programs to the extent appropriate for each program and consistent with applicable law.

(iii) To support employees whose work is monitored or augmented by AI in being compensated appropriately for all of their work time, the Secretary of Labor shall issue guidance to make clear that employers that deploy AI to monitor or augment employees' work must continue to comply with protections that ensure that workers are compensated for their hours worked, as defined under the Fair Labor Standards Act of 1938, 29 U.S.C. 201 et seq., and other legal requirements.

(c) To foster a diverse AI-ready workforce, the Director of NSF shall prioritize available resources to support AI-related education and AI-related workforce development through existing programs. The Director shall additionally consult with agencies, as appropriate, to identify further opportunities for agencies to allocate resources for those purposes. The actions by the Director shall use appropriate fellowship programs and awards for these purposes.

Sec. 7. Advancing Equity and Civil Rights.

7.1. Strengthening AI and Civil Rights in the Criminal Justice System. (a) To address unlawful discrimination and other harms that may be exacerbated by AI, the Attorney General shall:

(i) consistent with Executive Order 12250 of November 2, 1980 (Leadership and Coordination of Nondiscrimination Laws), Executive Order 14091, and 28 C.F.R. 0.50-51, coordinate with and support agencies in their implementation and enforcement of existing Federal laws to address civil rights and civil liberties violations and discrimination related to AI;

(ii) direct the Assistant Attorney General in charge of the Civil Rights Division to convene, within 90 days of the date of this order, a meeting of the heads of Federal civil rights offices - for which meeting the heads of civil rights offices within independent regulatory agencies will be encouraged to join - to discuss comprehensive use of their respective authorities and offices to: prevent and address discrimination in the use of automated systems, including algorithmic discrimination; increase coordination between the Department of Justice's Civil Rights Division and Federal civil rights offices concerning issues related to AI and algorithmic discrimination; improve external stakeholder engagement to promote public awareness of potential discriminatory uses and effects of AI; and develop, as appropriate, additional training, technical assistance, guidance, or other resources; and

(iii) consider providing, as appropriate and consistent with applicable law, guidance, technical assistance, and training to State, local, Tribal, and territorial investigators and prosecutors on best practices for investigating and prosecuting civil rights violations and discrimination related to automated systems, including AI.

(b) To promote the equitable treatment of individuals and adhere to the Federal Government's fundamental obligation to ensure fair and impartial justice for all, with respect to the use of AI in the criminal justice system, the Attorney General shall, in consultation with the Secretary of Homeland Security and the Director of OSTP:

(i) within 365 days of the date of this order, submit to the President a report that addresses the use of AI in the criminal justice system, including any use in:

(A) sentencing;

(B) parole, supervised release, and probation;

(C) bail, pretrial release, and pretrial detention;

(D) risk assessments, including pretrial, earned time, and early release or transfer to home-confinement determinations;

(E) police surveillance;

(F) crime forecasting and predictive policing, including the ingestion of historical crime data into AI systems to predict high-density "hot spots";

(G) prison-management tools; and

(H) forensic analysis;

(ii) within the report set forth in subsection 7.1(b)(i) of this section:

(A) identify areas where AI can enhance law enforcement efficiency and accuracy, consistent with protections for privacy, civil rights, and civil liberties; and

(B) recommend best practices for law enforcement agencies, including safeguards and appropriate use limits for AI, to address the concerns set forth in section 13(e)(i) of Executive Order 14074 as well as the best practices and the guidelines set forth in section 13(e)(iii) of Executive Order 14074; and

(iii) supplement the report set forth in subsection 7.1(b)(i) of this section as appropriate with recommendations to the President, including with respect to requests for necessary legislation.

(c) To advance the presence of relevant technical experts and expertise (such as machine-learning engineers, software and infrastructure engineering, data privacy experts, data scientists, and user experience researchers) among law enforcement professionals:

(i) The interagency working group created pursuant to section 3 of Executive Order 14074 shall, within 180 days of the date of this order, identify and share best practices for recruiting and hiring law enforcement professionals who have the technical skills mentioned in subsection 7.1(c) of this section, and for training law enforcement professionals about responsible application of AI.

(ii) Within 270 days of the date of this order, the Attorney General shall, in consultation with the Secretary of Homeland Security, consider those best practices and the guidance developed under section 3(d) of Executive Order 14074 and, if necessary, develop additional general recommendations for State, local, Tribal, and territorial law enforcement agencies and criminal justice agencies seeking to recruit, hire, train, promote, and retain highly qualified and service-oriented officers and staff with relevant technical knowledge. In considering this guidance, the Attorney General shall consult with State, local, Tribal, and territorial law enforcement agencies, as appropriate.

(iii) Within 365 days of the date of this order, the Attorney General shall review the work conducted pursuant to section 2(b) of Executive Order 14074 and, if appropriate, reassess the existing capacity to investigate law enforcement deprivation of rights under color of law resulting from the use of AI, including through improving and increasing training of Federal law enforcement officers, their supervisors, and Federal prosecutors on how to investigate and prosecute cases related to AI involving the deprivation of rights under color of law pursuant to 18 U.S.C. 242.

7.2. Protecting Civil Rights Related to Government Benefits and Programs. (a) To advance equity and civil rights, consistent with the directives of Executive Order 14091, and in addition to complying with the guidance on Federal Government use of AI issued pursuant to section 10.1(b) of this order, agencies shall use their respective civil rights and civil liberties offices and authorities - as appropriate and consistent with applicable law - to prevent and address unlawful discrimination and other harms that result from uses of AI in Federal Government programs and benefits administration. This directive does not apply to agencies' civil or criminal enforcement authorities. Agencies shall consider opportunities to ensure that their respective civil rights and civil liberties offices are appropriately consulted on agency decisions regarding the design, development, acquisition, and use of AI in Federal Government programs and benefits administration. To further these objectives, agencies shall also consider opportunities to increase coordination, communication, and engagement about AI as appropriate with community-based organizations; civil-rights and civil-liberties organizations; academic institutions; industry; State, local, Tribal, and territorial governments; and other stakeholders.

(b) To promote equitable administration of public benefits:

(i) The Secretary of HHS shall, within 180 days of the date of this order and in consultation with relevant agencies, publish a plan, informed by the guidance issued pursuant to section 10.1(b) of this order, addressing the use of automated or algorithmic systems in the implementation by States and localities of public benefits and services administered by the Secretary, such as to promote: assessment of access to benefits by qualified recipients; notice to recipients about the presence of such systems; regular evaluation to detect unjust denials; processes to retain appropriate levels of discretion of expert agency staff; processes to appeal denials to human reviewers; and analysis of whether algorithmic systems in use by benefit programs achieve equitable and just outcomes.

(ii) The Secretary of Agriculture shall, within 180 days of the date of this order and as informed by the guidance issued pursuant to section 10.1(b) of this order, issue guidance to State, local, Tribal, and territorial public-benefits administrators on the use of automated or algorithmic systems in implementing benefits or in providing customer support for benefit programs administered by the Secretary, to ensure that programs using those systems:

(A) maximize program access for eligible recipients;

(B) employ automated or algorithmic systems in a manner consistent with any requirements for using merit systems personnel in public-benefits programs;

(C) identify instances in which reliance on automated or algorithmic systems would require notification by the State, local, Tribal, or territorial government to the Secretary;

(D) identify instances when applicants and participants can appeal benefit determinations to a human reviewer for reconsideration and can receive other customer support from a human being;

(E) enable auditing and, if necessary, remediation of the logic used to arrive at an individual decision or determination to facilitate the evaluation of appeals; and

(F) enable the analysis of whether algorithmic systems in use by benefit programs achieve equitable outcomes.

7.3. Strengthening AI and Civil Rights in the Broader Economy. (a) Within 365 days of the date of this order, to prevent unlawful discrimination from AI used for hiring, the Secretary of Labor shall publish guidance for Federal contractors regarding nondiscrimination in hiring involving AI and other technology-based hiring systems.

(b) To address discrimination and biases against protected groups in housing markets and consumer financial markets, the Director of the Federal Housing Finance Agency and the Director of the Consumer Financial Protection Bureau are encouraged to consider using their authorities, as they deem appropriate, to require their respective regulated entities, where possible, to use appropriate methodologies including AI tools to ensure compliance with Federal law and:

(i) evaluate their underwriting models for bias or disparities affecting protected groups; and

(ii) evaluate automated collateral-valuation and appraisal processes in ways that minimize bias.

(c) Within 180 days of the date of this order, to combat unlawful discrimination enabled by automated or algorithmic tools used to make decisions about access to housing and in other real estate-related transactions, the Secretary of Housing and Urban Development shall, and the Director of the Consumer Financial Protection Bureau is encouraged to, issue additional guidance:

(i) addressing the use of tenant screening systems in ways that may violate the Fair Housing Act (Public Law 90-284), the Fair Credit Reporting Act (Public Law 91-508), or other relevant Federal laws, including how the use of data, such as criminal records, eviction records, and credit information, can lead to discriminatory outcomes in violation of Federal law; and

(ii) addressing how the Fair Housing Act, the Consumer Financial Protection Act of 2010 (title X of Public Law 111-203), or the Equal Credit Opportunity Act (Public Law 93-495) apply to the advertising of housing, credit, and other real estate-related transactions through digital platforms, including those that use algorithms to facilitate advertising delivery, as well as on best practices to avoid violations of Federal law.

(d) To help ensure that people with disabilities benefit from AI's promise while being protected from its risks, including unequal treatment from the use of biometric data like gaze direction, eye tracking, gait analysis, and hand motions, the Architectural and Transportation Barriers Compliance Board is encouraged, as it deems appropriate, to solicit public participation and conduct community engagement; to issue technical assistance and recommendations on the risks and benefits of AI in using biometric data as an input; and to provide people with disabilities access to information and communication technology and transportation services.

Sec. 8. Protecting Consumers, Patients, Passengers, and Students. (a) Independent regulatory agencies are encouraged, as they deem appropriate, to consider using their full range of authorities to protect American consumers from fraud, discrimination, and threats to privacy and to address other risks that may arise from the use of AI, including risks to financial stability, and to consider rulemaking, as well as emphasizing or clarifying where existing regulations and guidance apply to AI, including clarifying the responsibility of regulated entities to conduct due diligence on and monitor any third-party AI services they use, and emphasizing or clarifying requirements and expectations related to the transparency of AI models and regulated entities' ability to explain their use of AI models.

(b) To help ensure the safe, responsible deployment and use of AI in the healthcare, public-health, and human-services sectors:

(i) Within 90 days of the date of this order, the Secretary of HHS shall, in consultation with the Secretary of Defense and the Secretary of Veterans Affairs, establish an HHS AI Task Force that shall, within 365 days of its creation, develop a strategic plan that includes policies and frameworks - possibly including regulatory action, as appropriate - on responsible deployment and use of AI and AI-enabled technologies in the health and human services sector (including research and discovery, drug and device safety, healthcare delivery and financing, and public health), and identify appropriate guidance and
resources to promote that deployment, including in the following areas:

(A) development, maintenance, and use of predictive and generative AI-enabled technologies in healthcare delivery and financing - including quality measurement, performance improvement, program integrity, benefits administration, and patient experience - taking into account considerations such as appropriate human oversight of the application of AI-generated output;

(B) long-term safety and real-world performance monitoring of AI-enabled technologies in the health and human services sector, including clinically relevant or significant modifications and performance across population groups, with a means to communicate product updates to regulators, developers, and users;

(C) incorporation of equity principles in AI-enabled technologies used in the health and human services sector, using disaggregated data on affected populations and representative population data sets when developing new models, monitoring algorithmic performance against discrimination and bias in existing models, and helping to identify and mitigate discrimination and bias in current systems;

(D) incorporation of safety, privacy, and security standards into the software-development lifecycle for protection of personally identifiable information, including measures to address AI-enhanced cybersecurity threats in the health and human services sector;

(E) development, maintenance, and availability of documentation to help users determine appropriate and safe uses of AI in local settings in the health and human services sector;

(F) work to be done with State, local, Tribal, and territorial health and human services agencies to advance positive use cases and best practices for use of AI in local settings; and

(G) identification of uses of AI to promote workplace efficiency and satisfaction in the health and human services sector, including reducing administrative burdens.

(ii) Within 180 days of the date of this order, the Secretary of HHS shall direct HHS components, as the Secretary of HHS deems appropriate, to develop a strategy, in consultation with relevant agencies, to determine whether AI-enabled technologies in the health and human services sector maintain appropriate levels of quality, including, as appropriate, in the areas described in subsection (b)(i) of this section. This work shall include the development of AI assurance policy - to evaluate important aspects of the performance of AI-enabled healthcare tools - and infrastructure needs for enabling pre-market assessment and post-market oversight of AI-enabled healthcare-technology algorithmic system performance against real-world data.

(iii) Within 180 days of the date of this order, the Secretary of HHS shall, in consultation with relevant agencies as the Secretary of HHS deems appropriate, consider appropriate actions to advance the prompt understanding of, and compliance with, Federal nondiscrimination laws by health and human services providers that receive Federal financial assistance, as well as how those laws relate to AI. Such actions may include:

(A) convening and providing technical assistance to health and human services providers and payers about their obligations under Federal nondiscrimination and privacy laws as they relate to AI and the potential consequences of noncompliance; and

(B) issuing guidance, or taking other action as appropriate, in response to any complaints or other reports of noncompliance with Federal nondiscrimination and privacy laws as they relate to AI.

(iv) Within 365 days of the date of this order, the Secretary of HHS shall, in consultation with the Secretary of Defense and the Secretary of Veterans Affairs, establish an AI safety program that, in partnership with voluntary federally listed Patient Safety Organizations:

(A) establishes a common framework for approaches to identifying and capturing clinical errors resulting from AI deployed in healthcare settings as well as specifications for a central tracking repository for associated incidents that cause harm, including through bias or discrimination, to patients, caregivers, or other parties;

(B) analyzes captured data and generated evidence to develop, wherever appropriate, recommendations, best practices, or other informal guidelines aimed at avoiding these harms; and

(C) disseminates those recommendations, best practices, or other informal guidance to appropriate stakeholders, including healthcare providers.

(v) Within 365 days of the date of this order, the Secretary of HHS shall develop a strategy for regulating the use of AI or AI-enabled tools in drug-development processes. The strategy shall, at a minimum:

(A) define the objectives, goals, and high-level principles required for appropriate regulation throughout each phase of drug development;

(B) identify areas where future rulemaking, guidance, or additional statutory authority may be necessary to implement such a regulatory system;

(C) identify the existing budget, resources, personnel, and potential for new public/private partnerships necessary for such a regulatory system; and

(D) consider risks identified by the actions undertaken to implement section 4 of this order.

(c) To promote the safe and responsible development and use of AI in the transportation sector, in consultation with relevant agencies:

(i) Within 30 days of the date of this order, the Secretary of Transportation shall direct the Nontraditional and Emerging Transportation Technology (NETT) Council to assess the need for information, technical assistance, and guidance regarding the use of AI in transportation. The Secretary of Transportation shall further direct the NETT Council, as part of any such efforts, to:

(A) support existing and future initiatives to pilot transportation-related applications of AI, as they align with policy priorities articulated in the Department of Transportation's (DOT) Innovation Principles, including, as appropriate, through technical assistance and connecting stakeholders;

(B) evaluate the outcomes of such pilot programs in order to assess when DOT, or other Federal or State agencies, have sufficient information to take regulatory actions, as appropriate, and recommend appropriate actions when that information is available; and

(C) establish a new DOT Cross-Modal Executive Working Group, which will consist of members from different divisions of DOT and coordinate applicable work among these divisions, to solicit and use relevant input from appropriate stakeholders.

(ii) Within 90 days of the date of this order, the Secretary of Transportation shall direct appropriate Federal Advisory Committees of the DOT to provide advice on the safe and responsible use of AI in transportation. The committees shall include the Advanced Aviation Advisory Committee, the Transforming Transportation Advisory Committee, and the Intelligent Transportation Systems Program Advisory Committee.

(iii) Within 180 days of the date of this order, the Secretary of Transportation shall direct the Advanced Research Projects Agency-Infrastructure (ARPA-I) to explore the transportation-related opportunities and challenges of AI - including regarding software-defined AI enhancements impacting autonomous mobility ecosystems. The Secretary of Transportation shall further encourage ARPA-I to prioritize the allocation of grants to those opportunities, as appropriate. The work tasked to ARPA-I shall include soliciting input on these topics through a public consultation process, such as an RFI.

(d) To help ensure the responsible development and deployment of AI in the education sector, the Secretary of Education shall, within 365 days of the date of this order, develop resources, policies, and guidance regarding AI. These resources shall address safe, responsible, and nondiscriminatory uses of AI in education, including the impact AI systems have on vulnerable and underserved communities, and shall be developed in consultation with stakeholders as appropriate. They shall also include the development of an "AI toolkit" for education leaders implementing recommendations from the Department of Education's AI and the Future of Teaching and Learning report, including appropriate human review of AI decisions, designing AI systems to enhance trust and safety and align with privacy-related laws and regulations in the educational context, and developing education-specific guardrails.

(e) The Federal Communications Commission is encouraged to consider actions related to how AI will affect communications networks and consumers, including by:

(i) examining the potential for AI to improve spectrum management, increase the efficiency of non-Federal spectrum usage, and expand opportunities for the sharing of non-Federal spectrum;

(ii) coordinating with the National Telecommunications and Information Administration to create opportunities for sharing spectrum between Federal and non-Federal spectrum operations;

(iii) providing support for efforts to improve network security, resiliency, and interoperability using next-generation technologies that incorporate AI, including self-healing networks, 6G, and Open RAN; and

(iv) encouraging, including through rulemaking, efforts to combat unwanted robocalls and robotexts that are facilitated or exacerbated by AI and to deploy AI technologies that better serve consumers by blocking unwanted robocalls and robotexts.

Sec. 9. Protecting Privacy. (a) To mitigate privacy risks potentially exacerbated by AI - including by AI's facilitation of the collection or use of information about individuals, or the making of inferences about individuals - the Director of OMB shall:

(i) evaluate and take steps to identify commercially available information (CAI) procured by agencies, particularly CAI that contains personally identifiable information and including CAI procured from data brokers and CAI procured and processed indirectly through vendors, in appropriate agency inventory and reporting processes (other than when it is used for the purposes of national security);

(ii) evaluate, in consultation with the Federal Privacy Council and the Interagency Council on Statistical Policy, agency standards and procedures associated with the collection, processing, maintenance, use, sharing, dissemination, and disposition of CAI that contains personally identifiable information (other than when it is used for the purposes of national security) to inform potential guidance to agencies on ways to mitigate privacy and confidentiality risks from agencies' activities related to CAI;

(iii) within 180 days of the date of this order, in consultation with the Attorney General, the Assistant to the President for Economic Policy, and the Director of OSTP, issue an RFI to inform potential revisions to guidance to agencies on implementing the privacy provisions of the E-Government Act of 2002 (Public Law 107-347). The RFI shall seek feedback regarding how privacy impact assessments may be more effective at mitigating privacy risks, including those that are further exacerbated by AI; and

(iv) take such steps as are necessary and appropriate, consistent with applicable law, to support and advance the near-term actions and long-term strategy identified through the RFI process, including issuing new or updated guidance or RFIs or consulting other agencies or the Federal Privacy Council.

(b) Within 365 days of the date of this order, to better enable agencies to use PETs to safeguard Americans' privacy from the potential threats exacerbated by AI, the Secretary of Commerce, acting through the Director of NIST, shall create guidelines for agencies to evaluate the efficacy of differential-privacy-guarantee protections, including for AI. The guidelines shall, at a minimum, describe the significant factors that bear on differential-privacy safeguards and common risks to realizing differential privacy in practice.

(c) To advance research, development, and implementation related to PETs:

(i) Within 120 days of the date of this order, the Director of NSF, in collaboration with the Secretary of Energy, shall fund the creation of a Research Coordination Network (RCN) dedicated to advancing privacy research and, in particular, the development, deployment, and scaling of PETs. The RCN shall serve to enable privacy researchers to share information, coordinate and collaborate in research, and develop standards for the privacy-research community.

(ii) Within 240 days of the date of this order, the Director of NSF shall engage with agencies to identify ongoing work and potential opportunities to incorporate PETs into their operations. The Director of NSF shall, where feasible and appropriate, prioritize research - including efforts to translate research discoveries into practical applications - that encourage the adoption of leading-edge PETs solutions for agencies' use, including through research engagement through the RCN described in subsection (c)(i) of this section.

(iii) The Director of NSF shall use the results of the United States-United Kingdom PETs Prize Challenge to inform the approaches taken, and opportunities identified, for PETs research and adoption.

Sec. 10. Advancing Federal Government Use of AI.

10.1. Providing Guidance for AI Management. (a) To coordinate the use of AI across the Federal Government, within 60 days of the date of this order and on an ongoing basis as necessary, the Director of OMB shall convene and chair an interagency council to coordinate the development and use of AI in agencies' programs and operations, other than the use of AI in national security systems. The Director of OSTP shall serve as Vice Chair for the interagency council. The interagency council's membership shall include, at minimum, the heads of the agencies identified in 31 U.S.C. 901(b), the Director of National Intelligence, and other agencies as identified by the Chair. Until agencies designate their permanent Chief AI Officers consistent with the guidance described in subsection 10.1(b) of this section, they shall be represented on the interagency council by an appropriate official at the Assistant Secretary level or equivalent, as determined by the head of each agency.

(b) To provide guidance on Federal Government use of AI, within 150 days of the date of this order and updated periodically thereafter, the Director of OMB, in coordination with the Director of OSTP, and in consultation with the interagency council established in subsection 10.1(a) of this section, shall issue guidance to agencies to strengthen the effective and appropriate use of AI, advance AI innovation, and manage risks from AI in the Federal Government. The Director of OMB's guidance shall specify, to the extent appropriate and consistent with applicable law:

(i) the requirement to designate at each agency within 60 days of the issuance of the guidance a Chief Artificial Intelligence Officer who shall hold primary responsibility in their agency, in coordination with other responsible officials, for coordinating their agency's use of AI, promoting AI innovation in their agency, managing risks from their agency's use of AI, and carrying out the responsibilities described in section 8(c) of Executive Order 13960 of December 3, 2020 (Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government), and section 4(b) of Executive Order 14091;

(ii) the Chief Artificial Intelligence Officers' roles, responsibilities, seniority, position, and reporting structures;

(iii) for the agencies identified in 31 U.S.C. 901(b), the creation of internal Artificial Intelligence Governance Boards, or other appropriate mechanisms, at each agency within 60 days of the issuance of the guidance to coordinate and govern AI issues through relevant senior leaders from across the agency;

(iv) required minimum risk-management practices for Government uses of AI that impact people's rights or safety, including, where appropriate, the following practices derived from OSTP's Blueprint for an AI Bill of Rights and the NIST AI Risk Management Framework: conducting public consultation; assessing data quality; assessing and mitigating disparate impacts and algorithmic discrimination; providing notice of the use of AI; continuously monitoring and evaluating deployed AI; and granting human consideration and remedies for adverse decisions made using AI;

(v) specific Federal Government uses of AI that are presumed by default to impact rights or safety;

(vi) recommendations to agencies to reduce barriers to the responsible use of AI, including barriers related to information technology infrastructure, data, workforce, budgetary restrictions, and cybersecurity processes;

(vii) requirements that agencies identified in 31 U.S.C. 901(b) develop AI strategies and pursue high-impact AI use cases;

(viii) in consultation with the Secretary of Commerce, the Secretary of Homeland Security, and the heads of other appropriate agencies as determined by the Director of OMB, recommendations to agencies regarding:

(A) external testing for AI, including AI red-teaming for generative AI, to be developed in coordination with the Cybersecurity and Infrastructure Security Agency;

(B) testing and safeguards against discriminatory, misleading, inflammatory, unsafe, or deceptive outputs, as well as against producing child sexual abuse material and against producing non-consensual intimate imagery of real individuals (including intimate digital depictions of the body or body parts of an identifiable individual), for generative AI;

(C) reasonable steps to watermark or otherwise label output from generative AI;

(D) application of the mandatory minimum risk-management practices defined under subsection 10.1(b)(iv) of this section to procured AI;

(E) independent evaluation of vendors' claims concerning both the effectiveness and risk mitigation of their AI offerings;

(F) documentation and oversight of procured AI;

(G) maximizing the value to agencies when relying on contractors to use and enrich Federal Government data for the purposes of AI development and operation;

(H) provision of incentives for the continuous improvement of procured AI; and

(I) training on AI in accordance with the principles set out in this order and in other references related to AI listed herein; and

(ix) requirements for public reporting on compliance with this guidance.

(c) To track agencies' AI progress, within 60 days of the issuance of the guidance established in subsection 10.1(b) of this section and updated periodically thereafter, the Director of OMB shall develop a method for agencies to track and assess their ability to adopt AI into their programs and operations, manage its risks, and comply with Federal policy on AI. This method should draw on existing related efforts as appropriate and should address, as appropriate and consistent with applicable law, the practices, processes, and capabilities necessary for responsible AI adoption, training, and governance across, at a minimum, the areas of information technology infrastructure, data, workforce, leadership, and risk management.

(d) To assist agencies in implementing the guidance to be established in subsection 10.1(b) of this section:

(i) within 90 days of the issuance of the guidance, the Secretary of Commerce, acting through the Director of NIST, and in coordination with the Director of OMB and the Director of OSTP, shall develop guidelines, tools, and practices to support implementation of the minimum risk-management practices described in subsection 10.1(b)(iv) of this section; and

(ii) within 180 days of the issuance of the guidance, the Director of OMB shall develop an initial means to ensure that agency contracts for the acquisition of AI systems and services align with the guidance described in subsection 10.1(b) of this section and advance the other aims identified in section 7224(d)(1) of the Advancing American AI Act (Public Law 117-263, div. G, title LXXII, subtitle B).

(e) To improve transparency for agencies' use of AI, the Director of OMB shall, on an annual basis, issue instructions to agencies for the collection, reporting, and publication of agency AI use cases, pursuant to section 7225(a) of the Advancing American AI Act. Through these instructions, the Director shall, as appropriate, expand agencies' reporting on how they are managing risks from their AI use cases and update or replace the guidance originally established in section 5 of Executive Order 13960.

(f) To advance the responsible and secure use of generative AI in the Federal Government:

(i) As generative AI products become widely available and common in online platforms, agencies are discouraged from imposing broad general bans or blocks on agency use of generative AI. Agencies should instead limit access, as necessary, to specific generative AI services based on specific risk assessments; establish guidelines and limitations on the appropriate use of generative AI; and, with appropriate safeguards in place, provide their personnel and programs with access to secure and reliable generative AI capabilities, at least for the purposes of experimentation and routine tasks that carry a low risk of impacting Americans' rights. To protect Federal Government information, agencies are also encouraged to employ risk-management practices, such as training their staff on proper use, protection, dissemination, and disposition of Federal information; negotiating appropriate terms of service with vendors; implementing measures designed to ensure compliance with record-keeping, cybersecurity, confidentiality, privacy, and data protection requirements; and deploying other measures to prevent misuse of Federal Government information in generative AI.

(ii) Within 90 days of the date of this order, the Administrator of General Services, in coordination with the Director of OMB, and in consultation with the Federal Secure Cloud Advisory Committee and other relevant agencies as the Administrator of General Services may deem appropriate, shall develop and issue a framework for prioritizing critical and emerging technologies offerings in the Federal Risk and Authorization Management Program authorization process, starting with generative AI offerings that have the primary purpose of providing large language model-based chat interfaces, code-generation and debugging tools, and associated application programming interfaces, as well as prompt-based image generators. This framework shall apply for no less than 2 years from the date of its issuance. Agency Chief Information Officers, Chief Information Security Officers, and authorizing officials are also encouraged to prioritize generative AI and other critical and emerging technologies in granting authorities for agency operation of information technology systems and any other applicable release or oversight processes, using continuous authorizations and approvals wherever feasible.

(iii) Within 180 days of the date of this order, the Director of the Office of Personnel Management (OPM), in coordination with the Director of OMB, shall develop guidance on the use of generative AI for work by the Federal workforce.

(g) Within 30 days of the date of this order, to increase agency investment in AI, the Technology Modernization Board shall consider, as it deems appropriate and consistent with applicable law, prioritizing funding for AI projects for the Technology Modernization Fund for a period of at least 1 year. Agencies are encouraged to submit to the Technology Modernization Fund project funding proposals that include AI - and particularly generative AI - in service of mission delivery.

(h) Within 180 days of the date of this order, to facilitate agencies' access to commercial AI capabilities, the Administrator of General Services, in coordination with the Director of OMB, and in collaboration with the Secretary of Defense, the Secretary of Homeland Security, the Director of National Intelligence, the Administrator of the National Aeronautics and Space Administration, and the head of any other agency identified by the Administrator of General Services, shall take steps consistent with applicable law to facilitate access to Federal Government-wide acquisition solutions for specified types of AI services and products, such as through the creation of a resource guide or other tools to assist the acquisition workforce. Specified types of AI capabilities shall include generative AI and specialized computing infrastructure.

(i) The initial means, instructions, and guidance issued pursuant to subsections 10.1(a)-(h) of this section shall not apply to AI when it is used as a component of a national security system, which shall be addressed by the proposed National Security Memorandum described in subsection 4.8 of this order.

10.2. Increasing AI Talent in Government. (a) Within 45 days of the date of this order, to plan a national surge in AI talent in the Federal Government, the Director of OSTP and the Director of OMB, in consultation with the Assistant to the President for National Security Affairs, the Assistant to the President for Economic Policy, the Assistant to the President and Domestic Policy Advisor, and the Assistant to the President and Director of the Gender Policy Council, shall identify priority mission areas for increased Federal Government AI talent, the types of talent that are highest priority to recruit and develop to ensure adequate implementation of this order and use of relevant enforcement and regulatory authorities to address AI risks, and accelerated hiring pathways.

(b) Within 45 days of the date of this order, to coordinate rapid advances in the capacity of the Federal AI workforce, the Assistant to the President and Deputy Chief of Staff for Policy, in coordination with the Director of OSTP and the Director of OMB, and in consultation with the National Cyber Director, shall convene an AI and Technology Talent Task Force, which shall include the Director of OPM, the Director of the General Services Administration's Technology Transformation Services, a representative from the Chief Human Capital Officers Council, the Assistant to the President for Presidential Personnel, members of appropriate agency technology talent programs, a representative of the Chief Data Officer Council, and a representative of the interagency council convened under subsection 10.1(a) of this section. The Task Force's purpose shall be to accelerate and track the hiring of AI and AI-enabling talent across the Federal Government, including through the following actions:

(i) within 180 days of the date of this order, tracking and reporting progress to the President on increasing AI capacity across the Federal Government, including submitting to the President a report and recommendations for further increasing capacity;

(ii) identifying and circulating best practices for agencies to attract, hire, retain, train, and empower AI talent, including diversity, inclusion, and accessibility best practices, as well as to plan and budget adequately for AI workforce needs;

(iii) coordinating, in consultation with the Director of OPM, the use of fellowship programs and agency technology-talent programs and human-capital teams to build hiring capabilities, execute hires, and place AI talent to fill staffing gaps; and

(iv) convening a cross-agency forum for ongoing collaboration between AI professionals to share best practices and improve retention.

(c) Within 45 days of the date of this order, to advance existing Federal technology talent programs, the United States Digital Service, Presidential Innovation Fellowship, United States Digital Corps, OPM, and technology talent programs at agencies, with support from the AI and Technology Talent Task Force described in subsection 10.2(b) of this section, as appropriate and permitted by law, shall develop and begin to implement plans to support the rapid recruitment of individuals as part of a Federal Government-wide AI talent surge to accelerate the placement of key AI and AI-enabling talent in high-priority areas and to advance agencies' data and technology strategies.

(d) To meet the critical hiring need for qualified personnel to execute the initiatives in this order, and to improve Federal hiring practices for AI talent, the Director of OPM, in consultation with the Director of OMB, shall:

(i) within 60 days of the date of this order, conduct an evidence-based review on the need for hiring and workplace flexibility, including Federal Government-wide direct-hire authority for AI and related data-science and technical roles, and, where the Director of OPM finds such authority is appropriate, grant it; this review shall include the following job series at all General Schedule (GS) levels: IT Specialist (2210), Computer Scientist (1550), Computer Engineer (0854), and Program Analyst (0343) focused on AI, and any subsequently developed job series derived from these job series;

(ii) within 60 days of the date of this order, consider authorizing the use of excepted service appointments under 5 C.F.R. 213.3102(i)(3) to address the need for hiring additional staff to implement directives of this order;

(iii) within 90 days of the date of this order, coordinate a pooled-hiring action informed by subject-matter experts and using skills-based assessments to support the recruitment of AI talent across agencies;

(iv) within 120 days of the date of this order, as appropriate and permitted by law, issue guidance for agency application of existing pay flexibilities or incentive pay programs for AI, AI-enabling, and other key technical positions to facilitate appropriate use of current pay incentives;

(v) within 180 days of the date of this order, establish guidance and policy on skills-based, Federal Government-wide hiring of AI, data, and technology talent in order to increase access to those with nontraditional academic backgrounds to Federal AI, data, and technology roles;

(vi) within 180 days of the date of this order, establish an interagency working group, staffed with both human-resources professionals and recruiting technical experts, to facilitate Federal Government-wide hiring of people with AI and other technical skills;

(vii) within 180 days of the date of this order, review existing Executive Core Qualifications (ECQs) for Senior Executive Service (SES) positions informed by data and AI literacy competencies and, within 365 days of the date of this order, implement new ECQs as appropriate in the SES assessment process;

(viii) within 180 days of the date of this order, complete a review of competencies for civil engineers (GS-0810 series) and, if applicable, other related occupations, and make recommendations for ensuring that adequate AI expertise and credentials in these occupations in the Federal Government reflect the increased use of AI in critical infrastructure; and

(ix) work with the Security, Suitability, and Credentialing Performance Accountability Council to assess mechanisms to streamline and accelerate personnel-vetting requirements, as appropriate, to support AI and fields related to other critical and emerging technologies.

(e) To expand the use of special authorities for AI hiring and retention, agencies shall use all appropriate hiring authorities, including Schedule A(r) excepted service hiring and direct-hire authority, as applicable and appropriate, to hire AI talent and AI-enabling talent rapidly. In addition to participating in OPM-led pooled hiring actions, agencies shall collaborate, where appropriate, on agency-led pooled hiring under the Competitive Service Act of 2015 (Public Law 114-137) and other shared hiring. Agencies shall also, where applicable, use existing incentives, pay-setting authorities, and other compensation flexibilities, similar to those used for cyber and information technology positions, for AI and data-science professionals, as well as plain-language job titles, to help recruit and retain these highly skilled professionals. Agencies shall ensure that AI and other related talent needs (such as technology governance and privacy) are reflected in strategic workforce planning and budget formulation.

(f) To facilitate the hiring of data scientists, the Chief Data Officer Council shall develop a position-description library for data scientists (job series 1560) and a hiring guide to support agencies in hiring data scientists.

(g) To help train the Federal workforce on AI issues, the head of each agency shall implement - or increase the availability and use of - AI training and familiarization programs for employees, managers, and leadership in technology as well as relevant policy, managerial, procurement, regulatory, ethical, governance, and legal fields. Such training programs should, for example, empower Federal employees, managers, and leaders to develop and maintain an operating knowledge of emerging AI technologies to assess opportunities to use these technologies to enhance the delivery of services to the public, and to mitigate risks associated with these technologies. Agencies that provide professional-development opportunities, grants, or funds for their staff should take appropriate steps to ensure that employees who do not serve in traditional technical roles, such as policy, managerial, procurement, or legal fields, are nonetheless eligible to receive funding for programs and courses that focus on AI, machine learning, data science, or other related subject areas.

(h) Within 180 days of the date of this order, to address gaps in AI talent for national defense, the Secretary of Defense shall submit a report to the President through the Assistant to the President for
National Security Affairs that includes:

(i) recommendations to address challenges in the Department of Defense's ability to hire certain noncitizens, including at the Science and Technology Reinvention Laboratories;

(ii) recommendations to clarify and streamline processes for accessing classified information for certain noncitizens through Limited Access Authorization at Department of Defense laboratories;

(iii) recommendations for the appropriate use of enlistment authority under 10 U.S.C. 504(b)(2) for experts in AI and other critical and emerging technologies; and

(iv) recommendations for the Department of Defense and the Department of Homeland Security to work together to enhance the use of appropriate authorities for the retention of certain noncitizens of vital importance to national security by the Department of Defense and the Department of Homeland Security.

Sec. 11. Strengthening American Leadership Abroad. (a) To strengthen United States leadership of global efforts to unlock AI's potential and meet its challenges, the Secretary of State, in coordination with the Assistant to the President for National Security Affairs, the Assistant to the President for Economic Policy, the Director of OSTP, and the heads of other relevant agencies as appropriate, shall:

(i) lead efforts outside of military and intelligence areas to expand engagements with international allies and partners in relevant bilateral, multilateral, and multi-stakeholder fora to advance those allies' and partners' understanding of existing and planned AI-related guidance and policies of the United States, as well as to enhance international collaboration; and

(ii) lead efforts to establish a strong international framework for managing the risks and harnessing the benefits of AI, including by encouraging international allies and partners to support voluntary commitments similar to those that United States companies have made in pursuit of these objectives and coordinating the activities directed by subsections (b), (c), (d), and (e) of this section, and to develop common regulatory and other accountability principles for foreign nations, including to manage the risk that AI systems pose.

(b) To advance responsible global technical standards for AI development and use outside of military and intelligence areas, the Secretary of Commerce, in coordination with the Secretary of State and the heads of other relevant agencies as appropriate, shall lead preparations for a coordinated effort with key international allies and partners and with standards development organizations, to drive the development and implementation of AI-related consensus standards, cooperation and coordination, and information sharing. In particular, the Secretary of Commerce shall:

(i) within 270 days of the date of this order, establish a plan for global engagement on promoting and developing AI standards, with lines of effort that may include:

(A) AI nomenclature and terminology;

(B) best practices regarding data capture, processing, protection, privacy, confidentiality, handling, and analysis;

(C) trustworthiness, verification, and assurance of AI systems; and

(D) AI risk management;

(ii) within 180 days of the date the plan is established, submit a report to the President on priority actions taken pursuant to the plan; and

(iii) ensure that such efforts are guided by principles set out in the NIST AI Risk Management Framework and United States Government National Standards Strategy for Critical and Emerging Technology.

(c) Within 365 days of the date of this order, to promote safe, responsible, and rights-affirming development and deployment of AI abroad:

(i) The Secretary of State and the Administrator of the United States Agency for International Development, in coordination with the Secretary of Commerce, acting through the director of NIST, shall publish an AI in Global Development Playbook that incorporates the AI Risk Management Framework's principles, guidelines, and best practices into the social, technical, economic, governance, human rights, and security conditions of contexts beyond United States borders. As part of this work, the Secretary of State and the Administrator of the United States Agency for International Development shall draw on lessons learned from programmatic uses of AI in global development.

(ii) The Secretary of State and the Administrator of the United States Agency for International Development, in collaboration with the Secretary of Energy and the Director of NSF, shall develop a Global AI Research Agenda to guide the objectives and implementation of AI-related research in contexts beyond United States borders. The Agenda shall:

(A) include principles, guidelines, priorities, and best practices aimed at ensuring the safe, responsible, beneficial, and sustainable global development and adoption of AI; and

(B) address AI's labor-market implications across international contexts, including by recommending risk mitigations.

(d) To address cross-border and global AI risks to critical infrastructure, the Secretary of Homeland Security, in coordination with the Secretary of State, and in consultation with the heads of other relevant agencies as the Secretary of Homeland Security deems appropriate, shall lead efforts with international allies and partners to enhance cooperation to prevent, respond to, and recover from potential critical infrastructure disruptions resulting from incorporation of AI into critical infrastructure systems or malicious use of AI.

(i) Within 270 days of the date of this order, the Secretary of Homeland Security, in coordination with the Secretary of State, shall develop a plan for multilateral engagements to encourage the adoption of the AI safety and security guidelines for use by critical infrastructure owners and operators developed in section 4.3(a) of this order.

(ii) Within 180 days of establishing the plan described in subsection (d)(i) of this section, the Secretary of Homeland Security shall submit a report to the President on priority actions to mitigate cross-border risks to critical United States infrastructure.

Sec. 12. Implementation. (a) There is established, within the Executive Office of the President, the White House Artificial Intelligence Council (White House AI Council). The function of the White House AI Council is to coordinate the activities of agencies across the Federal Government to ensure the effective formulation, development, communication, industry engagement related to, and timely implementation of AI-related policies, including policies set forth in this order.

(b) The Assistant to the President and Deputy Chief of Staff for Policy shall serve as Chair of the White House AI Council.

(c) In addition to the Chair, the White House AI Council shall consist of the following members, or their designees:

(i) the Secretary of State;

(ii) the Secretary of the Treasury;

(iii) the Secretary of Defense;

(iv) the Attorney General;

(v) the Secretary of Agriculture;

(vi) the Secretary of Commerce;

(vii) the Secretary of Labor;

(viii) the Secretary of HHS;

(ix) the Secretary of Housing and Urban Development;

(x) the Secretary of Transportation;

(xi) the Secretary of Energy;

(xii) the Secretary of Education;

(xiii) the Secretary of Veterans Affairs;

(xiv) the Secretary of Homeland Security;

(xv) the Administrator of the Small Business Administration;

(xvi) the Administrator of the United States Agency for International Development;

(xvii) the Director of National Intelligence;

(xviii) the Director of NSF;

(xix) the Director of OMB;

(xx) the Director of OSTP;

(xxi) the Assistant to the President for National Security Affairs;

(xxii) the Assistant to the President for Economic Policy;

(xxiii) the Assistant to the President and Domestic Policy Advisor;

(xxiv) the Assistant to the President and Chief of Staff to the Vice President;

(xxv) the Assistant to the President and Director of the Gender Policy Council;

(xxvi) the Chairman of the Council of Economic Advisers;

(xxvii) the National Cyber Director;

(xxviii) the Chairman of the Joint Chiefs of Staff; and

(xxix) the heads of such other agencies, independent regulatory agencies, and executive offices as the Chair may from time to time designate or invite to participate.

(d) The Chair may create and coordinate subgroups consisting of White House AI Council members or their designees, as appropriate.

Sec. 13. General Provisions. (a) Nothing in this order shall be construed to impair or otherwise affect:

(i) the authority granted by law to an executive department or agency, or the head thereof; or

(ii) the functions of the Director of the Office of Management and Budget relating to budgetary, administrative, or legislative proposals.

(b) This order shall be implemented consistent with applicable law and subject to the availability of appropriations.

(c) This order is not intended to, and does not, create any right or benefit, substantive or procedural, enforceable at law or in equity by any party against the United States, its departments, agencies, or entities, its officers, employees, or agents, or any other person.

JOSEPH R. BIDEN JR.

THE WHITE HOUSE,
October 30, 2023.

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Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence | The White House

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By the authority vested in me as President by the Constitution and the laws of the United States of America, it is hereby ordered as follows: Section 1. Purpose. Artificial intelligence (AI) holds extraordinary potential for both promise and peril. Responsible AI use has the potential to help solve urgent challenges…

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Michael V Drake Institute for Teaching and Learning - OSU 

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Michael V Drake Institute for Teaching and Learning - OSU 

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Artificial Intelligence Offers Opportunities and Challenges for Teaching: Perspectives from the Drake Institute - This is a much more proactive approach to incorporating large language models of Generative AI, like ChatGPT into instructional planning.

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Michael V. Drake Institute for Teaching and Learning

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Implications of recent and rapid advances in the development and availability of generative artificial intelligence (AI) systems such as ChatGPT are resounding across the landscape of teaching and learning. From discussions of academic integrity to efforts in course design, AI is quickly becoming an embedded element of the teaching and learning process that requires the acknowledgement and attention of instructors, instructional designers, and academic leaders.

The Drake Institute for Teaching and Learning recognizes the emerging opportunities and challenges that many instructors at Ohio State face in understanding, anticipating, and responding to AI in the context of teaching and is committed to supporting the needs of instructors in this space. The Institute also recognizes the highly complex and evolving nature of this issue as one that will ultimately require significant contributions from a large and diverse group of collaborators and stakeholders committed to excellence in teaching and student success in higher education.

In response to current concerns and questions of instructors at Ohio State, the Drake Institute, first and foremost, recommends continued reliance on known, evidence-based approaches to instruction in guiding decisions around embracing or otherwise addressing AI in the process of planning, implementing, and evaluating teaching and learning. To this end, the Institute offers the following broad suggestions for instructors and warmly invites questions, conversations, and consultations around these ideas:

1. Similar to most efforts in redesigning and iterating on instruction, instructors are encouraged to first reflect on course goals and objectives for student learning prior to determining if and how generative AI applications can be effectively integrated into instruction. This "backward design" approach (Wiggins and McTighe, 2005) can help clarify objectives, support alignment across class activities and assessments, and improve student learning and academic performance.

2. When goals and outcomes suggest opportunity for effective infusion of AI as part of the learning process, plan active learning approaches using evidence-based implementation strategies (Nguyen et al., 2021 and Andrews et al., 2022) that support student motivation and equity while minimizing potential student resistance. Ideas to get started include:

1. Establish and communicate clear expectations around the use of AI in the course, broadly, as well as within specific assignments and activities.

2. Intentionally discuss with students the purpose of planned activities with justification for, and reasoning behind, the infusion or exclusion of AI tools.

3. Approach facilitation of active learning engagements with a focus on expressing enthusiasm around the learning, creating an inclusive and welcoming learning environment, and enhancing motivation and excitement, particularly around AI as a tool for learning.

3. Evaluate and revise design elements of your course with transparency around AI at the forefront. From syllabus construction (Wheeler, Palmer and Aneece, 2019), to assignment design and delivery (Winkelmes et al., 2016), to assessment practices (Balloo et al., 2018), taking concrete steps to improve the transparency of purpose and expectations associated with various aspects of instruction can significantly improve experiences and outcomes for all students. A simple but potentially impactful first step can be small changes in the way assignments are presented to your students through use of the Transparency in Learning and Teaching, or TILT, framework (Winkelmes et al., 2016). This framework establishes new norms for introducing students to assignments in the course before they begin any work using a basic, three-step process:

1. State the purpose: For assignments that leverage or limit use of generative AI tools, compose a short narrative on, and discuss with students, the purpose of assignment, the skills that students will practice, and the knowledge that the assignment is designed to support. Clarify how the assignment connects to future coursework or work in the discipline and, where relevant, how AI might be expected to impact those areas now and in the future.

2. Define the task involved: List or describe the steps that a successful student would take in completing the assignment. This might involve explicitly describing when and how AI tools should and should not be utilized to support progress in the assignment. This step has the potential to transform the experience of students who might be in some way disadvantaged and unequipped with the experience and knowledge necessary to confidently begin the work assigned in the course.

3. Establish and engage students in understanding criteria for success: Create, or when possible, co-create with students a rubric or checklist that establishes the scope and quality of submission that is necessary to be successful on the assignment. Involve considerations of AI in this process and seek student input or feedback on policies regarding AI, particularly as they related to the assignment criteria. Consider allowing the students an opportunity to apply the criteria for success to sample work and reflect on what they learned from the experience. While adding a step to the assignment process, the potential for improving student outcomes through this type of exercise may be well worth the added investment.

4. Incorporate attention to instructional practices that involve AI in efforts to evaluate and reflect on teaching effectiveness. When instruction is revised or redesigned using strategies and approaches such as those described above, the impact of such changes on student learning and experience will not be immediately clear. Understanding implications of course and instructional redesign efforts related to AI will require intentional steps of evaluation and reflection. Fortunately, courses, by nature and design, offer a variety of data sources that can be used to measure and evaluate the impact of teaching. Instructors are encouraged to review and reflect on data to help inform changes to instruction over time. Several ideas to help in the evaluation and reflection process are outlined below.

1. Review student performance on AI-aligned summative assessments (exams, projects, papers, etc.). Summative assessments are those used to measure student learning after the learning has occurred and provide direct measures of student learning outcome achievement. For areas of the course in which instruction has either leveraged, limited, or otherwise explicitly addressed AI, student performance on relevant summative assessments or assessment items can serve as one line of data to help instructors better understand the effectiveness of the instruction provided.

2. Review formative assessment data related to AI. Formative assessments include activities and engagements that provide a measure of progress in learning while the learning occurs (e.g., quizzes, in-class activities, student reflections). Some such exercises used in the course might incorporate the use of generative AI tools. Monitoring student progress through a review of student submissions to these formative assessments can not only help to inform future changes to instruction but also create opportunities to provide students with supportive feedback.

3. Collect and consider mid-course student feedback on instruction. Student surveys or Institute services, such as the Small Group Instructional Diagnosis (SGID) provide clear opportunities for students to share their own thoughts and perceptions of what is, and what is not, working to support their learning in the course, and can be adjusted to explore issues and questions surrounding AI as a teaching and learning tool. The SGID service is available to all instructors teaching at Ohio State and provides valuable student feedback through a focus group-style effort during the term. To request a SGID for your course, simply e-mail the Drake Institute at drakeinstitute@osu.edu. To learn more about the SGID service and other opportunities for collecting and reviewieng student feedback, visit the Drake Institute's webpage on Using Feedback to Improve Teaching.

4. Reflect, personally, on perceptions of teaching effectiveness. When incorporating new approaches to instruction, such as those that involve AI, take time to step back and reflect on the experience, from an instructional perspective. How did the students respond? What observations were made during the instruction? Personal perspective, while not something to rely on alone, can serve as a valuable line of data in the larger effort to evaluate the impact of instruction.

The ideas presented above are offered in hopes of supporting instructors in navigating through new opportunities and challenges that emerge from increasing access to generative technologies and tools that leverage AI. These approaches are designed to foster motivation, engagement, equity, and academic integrity. However, they in no way eliminate risks of, or opportunities for, unethical and inappropriate student application of AI in course contexts. The Institute encourages instructors to work with the University Committee on Academic Misconduct (COAM) to address any instances of suspected misuse that might constitute academic misconduct.

The presence of generative AI tools is and will continue to be a reality that must be acknowledged and addressed with care. The Drake Institute is committed to sharing evidence-based strategies for addressing issues and challenges involved in the teaching and learning process and to working with all who teach and support teaching at Ohio State in advancing instructional excellence.

For questions or to request a consultation around teaching, please visit http://drakeinstitute.osu.edu or e-mail us at drakeinstitute@osu.edu.
Literature Cited:
Wiggins, G., & McTighe, J. (2005). Backward design. In, Understanding by Design (2nd ed., pp. 13-34). Pearson Merrill Prentice Hall.

Winkelmes, M., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., & Weavil, K.H. (2016). A teaching intervention that increases underserved college students' success. Peer Review, 18(1/2), 31-36.

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Artificial Intelligence Offers Opportunities and Challenges for Teaching: Perspectives from the Drake Institute

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News and evidence-based resources for all who teach at Ohio State.

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There is a free account for individual teachers - it does the prompt writing for you.

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MagicSchool.ai - AI for teachers - lesson planning and more!

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Join over a million teachers saving hours of time using the #1 AI Platform for Education, MagicSchool, to help lesson plan, differentiate, write assessments, write IEPs, communicate clearly, and more!

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What Is Generative AI ... And How It Can Save You Time And Energy Char Shryock Education Leadership Consultant http://bit.ly/LearnChatGPT Woodburn Local Schools

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When ChatGPT Became My Math Partner Char Shryock Education Consultant HQIM NW Regional Lead Specialist OCTM 2023 Math A Roaring Good Time October 12, 2023 http://bit.ly/OCTMChatGPT

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TurnItIn.com blog- Academic Integrity vs Plagiarism

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TurnItIn.com blog- Academic Integrity vs Plagiarism

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What is the difference between academic integrity and plagiarism?

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​In this blog post, we delve into the difference between “academic integrity” and “plagiarism.”

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A Padlet of Resources curated by Dr. Heather Brown

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750 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

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New Yorker Opinion Piece - There Is No AI

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New Yorker Opinion Piece - There Is No AI

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There are ways of controlling the new technology-but first we have to stop mythologizing it.

As a computer scientist, I don't like the term "A.I." In fact, I think it's misleading-maybe even a little dangerous. Everybody's already using the term, and it might seem a little late in the day to be arguing about it. But we're at the beginning of a new technological era-and the easiest way to mismanage a technology is to misunderstand it.

The term " artificial intelligence" has a long history-it was coined in the nineteen-fifties, in the early days of computers. More recently, computer scientists have grown up on movies like "The Terminator" and " The Matrix," and on characters like Commander Data, from "Star Trek: The Next Generation." These cultural touchstones have become an almost religious mythology in tech culture. It's only natural that computer scientists long to create A.I. and realize a long-held dream.

What's striking, though, is that many of the people who are pursuing the A.I. dream also worry that it might mean doomsday for mankind. It is widely stated, even by scientists at the very center of today's efforts, that what A.I. researchers are doing could result in the annihilation of our species, or at least in great harm to humanity, and soon. In a recent poll, half of A.I. scientists agreed that there was at least a ten-per-cent chance that the human race would be destroyed by A.I. Even my colleague and friend Sam Altman, who runs OpenAI, has made similar comments. Step into any Silicon Valley coffee shop and you can hear the same debate unfold: one person says that the new code is just code and that people are in charge, but another argues that anyone with this opinion just doesn't get how profound the new tech is. The arguments aren't entirely rational: when I ask my most fearful scientist friends to spell out how an A.I. apocalypse might happen, they often seize up from the paralysis that overtakes someone trying to conceive of infinity. They say things like "Accelerating progress will fly past us and we will not be able to conceive of what is happening."

I don't agree with this way of talking. Many of my friends and colleagues are deeply impressed by their experiences with the latest big models, like GPT-4, and are practically holding vigils to await the appearance of a deeper intelligence. My position is not that they are wrong but that we can't be sure; we retain the option of classifying the software in different ways.

The most pragmatic position is to think of A.I. as a tool, not a creature. My attitude doesn't eliminate the possibility of peril: however we think about it, we can still design and operate our new tech badly, in ways that can hurt us or even lead to our extinction. Mythologizing the technology only makes it more likely that we'll fail to operate it well-and this kind of thinking limits our imaginations, tying them to yesterday's dreams. We can work better under the assumption that there is no such thing as A.I. The sooner we understand this, the sooner we'll start managing our new technology intelligently.

If the new tech isn't true artificial intelligence, then what is it? In my view, the most accurate way to understand what we are building today is as an innovative form of social collaboration.

A program like OpenAI's GPT-4, which can write sentences to order, is something like a version of Wikipedia that includes much more data, mashed together using statistics. Programs that create images to order are something like a version of online image search, but with a system for combining the pictures. In both cases, it's people who have written the text and furnished the images. The new programs mash up work done by human minds. What's innovative is that the mashup process has become guided and constrained, so that the results are usable and often striking. This is a significant achievement and worth celebrating-but it can be thought of as illuminating previously hidden concordances between human creations, rather than as the invention of a new mind.

As far as I can tell, my view flatters the technology. After all, what is civilization but social collaboration? Seeing A.I. as a way of working together, rather than as a technology for creating independent, intelligent beings, may make it less mysterious-less like HAL 9000 or Commander Data. But that's good, because mystery only makes mismanagement more likely.

It's easy to attribute intelligence to the new systems; they have a flexibility and unpredictability that we don't usually associate with computer technology. But this flexibility arises from simple mathematics. A large language model like GPT-4 contains a cumulative record of how particular words coincide in the vast amounts of text that the program has processed. This gargantuan tabulation causes the system to intrinsically approximate many grammar patterns, along with aspects of what might be called authorial style. When you enter a query consisting of certain words in a certain order, your entry is correlated with what's in the model; the results can come out a little differently each time, because of the complexity of correlating billions of entries.

The non-repeating nature of this process can make it feel lively. And there's a sense in which it can make the new systems more human-centered. When you synthesize a new image with an A.I. tool, you may get a bunch of similar options and then have to choose from them; if you're a student who uses an L.L.M. to cheat on an essay assignment, you might read options generated by the model and select one. A little human choice is demanded by a technology that is non-repeating.

Many of the uses of A.I. that I like rest on advantages we gain when computers get less rigid. Digital stuff as we have known it has a brittle quality that forces people to conform to it, rather than assess it. We've all endured the agony of watching some poor soul at a doctor's office struggle to do the expected thing on a front-desk screen. The face contorts; humanity is undermined. The need to conform to digital designs has created an ambient expectation of human subservience. A positive spin on A.I. is that it might spell the end of this torture, if we use it well. We can now imagine a Web site that reformulates itself on the fly for someone who is color-blind, say, or a site that tailors itself to someone's particular cognitive abilities and styles. A humanist like me wants people to have more control, rather than be overly influenced or guided by technology. Flexibility may give us back some agency.

Still, despite these possible upsides, it's more than reasonable to worry that the new technology will push us around in ways we don't like or understand. Recently, some friends of mine circulated a petition asking for a pause on the most ambitious A.I. development. The idea was that we'd work on policy during the pause. The petition was signed by some in our community but not others. I found the notion too hazy-what level of progress would mean that the pause could end? Every week, I receive new but always vague mission statements from organizations seeking to initiate processes to set A.I. policy.

These efforts are well intentioned, but they seem hopeless to me. For years, I worked on the E.U.'s privacy policies, and I came to realize that we don't know what privacy is. It's a term we use every day, and it can make sense in context, but we can't nail it down well enough to generalize. The closest we have come to a definition of privacy is probably "the right to be left alone," but that seems quaint in an age when we are constantly dependent on digital services. In the context of A.I., "the right to not be manipulated by computation" seems almost correct, but doesn't quite say everything we'd like it to.

A.I.-policy conversations are dominated by terms like "alignment" (is what an A.I. "wants" aligned with what humans want?), "safety" (can we foresee guardrails that will foil a bad A.I.?), and "fairness" (can we forestall all the ways a program might treat certain people with disfavor?). The community has certainly accomplished much good by pursuing these ideas, but that hasn't quelled our fears. We end up motivating people to try to circumvent the vague protections we set up. Even though the protections do help, the whole thing becomes a game-like trying to outwit a sneaky genie. The result is that the A.I.-research community communicates the warning that their creations might still kill all of humanity soon, while proposing ever more urgent, but turgid, deliberative processes.

Recently, I tried an informal experiment, calling colleagues and asking them if there's anything specific on which we can all seem to agree. I've found that there is a foundation of agreement. We all seem to agree that deepfakes -false but real-seeming images, videos, and so on-should be labelled as such by the programs that create them. Communications coming from artificial people, and automated interactions that are designed to manipulate the thinking or actions of a human being, should be labelled as well. We also agree that these labels should come with actions that can be taken. People should be able to understand what they're seeing, and should have reasonable choices in return.

How can all this be done? There is also near-unanimity, I find, that the black-box nature of our current A.I. tools must end. The systems must be made more transparent. We need to get better at saying what is going on inside them and why. This won't be easy. The problem is that the large-model A.I. systems we are talking about aren't made of explicit ideas. There is no definite representation of what the system "wants," no label for when it is doing a particular thing, like manipulating a person. There is only a giant ocean of jello-a vast mathematical mixing. A writers'-rights group has proposed that real human authors be paid in full when tools like GPT are used in the scriptwriting process; after all, the system is drawing on scripts that real people have made. But when we use A.I. to produce film clips, and potentially whole movies, there won't necessarily be a screenwriting phase. A movie might be produced that appears to have a script, soundtrack, and so on, but it will have been calculated into existence as a whole. Similarly, no sketch precedes the generation of a painting from an illustration A.I. Attempting to open the black box by making a system spit out otherwise unnecessary items like scripts, sketches, or intentions will involve building another black box to interpret the first-an infinite regress.

At the same time, it's not true that the interior of a big model has to be a trackless wilderness. We may not know what an "idea" is from a formal, computational point of view, but there could be tracks made not of ideas but of people. At some point in the past, a real person created an illustration that was input as data into the model, and, in combination with contributions from other people, this was transformed into a fresh image. Big-model A.I. is made of people-and the way to open the black box is to reveal them.

This concept, which I've contributed to developing, is usually called "data dignity." It appeared, long before the rise of big-model "A.I.," as an alternative to the familiar arrangement in which people give their data for free in exchange for free services, such as internet searches or social networking. Data dignity is sometimes known as "data as labor" or "plurality research." The familiar arrangement has turned out to have a dark side: because of "network effects," a few platforms take over, eliminating smaller players, like local newspapers. Worse, since the immediate online experience is supposed to be free, the only remaining business is the hawking of influence. Users experience what seems to be a communitarian paradise, but they are targeted by stealthy and addictive algorithms that make people vain, irritable, and paranoid.

In a world with data dignity, digital stuff would typically be connected with the humans who want to be known for having made it. In some versions of the idea, people could get paid for what they create, even when it is filtered and recombined through big models, and tech hubs would earn fees for facilitating things that people want to do. Some people are horrified by the idea of capitalism online, but this would be a more honest capitalism. The familiar "free" arrangement has been a disaster.

One of the reasons the tech community worries that A.I. could be an existential threat is that it could be used to toy with people, just as the previous wave of digital technologies have been. Given the power and potential reach of these new systems, it's not unreasonable to fear extinction as a possible result. Since that danger is widely recognized, the arrival of big-model A.I. could be an occasion to reformat the tech industry for the better.

Implementing data dignity will require technical research and policy innovation. In that sense, the subject excites me as a scientist. Opening the black box will only make the models more interesting. And it might help us understand more about language, which is the human invention that truly impresses, and the one that we are still exploring after all these hundreds of thousands of years.

Could data dignity address the economic worries that are often expressed about A.I.? The main concern is that workers will be devalued or displaced. Publicly, techies will sometimes say that, in the coming years, people who work with A.I. will be more productive and will find new types of jobs in a more productive economy. (A worker might become a prompt engineer for A.I. programs, for instance-someone who collaborates with or controls an A.I.) And yet, in private, the same people will quite often say, "No, A.I. will overtake this idea of collaboration." No more remuneration for today's accountants, radiologists, truck drivers, writers, film directors, or musicians.

A data-dignity approach would trace the most unique and influential contributors when a big model provides a valuable output. For instance, if you ask a model for "an animated movie of my kids in an oil-painting world of talking cats on an adventure," then certain key oil painters, cat portraitists, voice actors, and writers-or their estates-might be calculated to have been uniquely essential to the creation of the new masterpiece. They would be acknowledged and motivated. They might even get paid.

There is a fledgling data-dignity research community, and here is an example of a debate within it: How detailed an accounting should data dignity attempt? Not everyone agrees. The system wouldn't necessarily account for the billions of people who have made ambient contributions to big models-those who have added to a model's simulated competence with grammar, for example. At first, data dignity might attend only to the small number of special contributors who emerge in a given situation. Over time, though, more people might be included, as intermediate rights organizations-unions, guilds, professional groups, and so on-start to play a role. People in the data-dignity community sometimes call these anticipated groups mediators of individual data ( MIDs) or data trusts. People need collective-bargaining power to have value in an online world-especially when they might get lost in a giant A.I. model. And when people share responsibility in a group, they self-police, reducing the need, or temptation, for governments and companies to censor or control from above. Acknowledging the human essence of big models might lead to a blossoming of new positive social institutions.

Data dignity is not just for white-collar roles. Consider what might happen if A.I.-driven tree-trimming robots are introduced. Human tree trimmers might find themselves devalued or even out of work. But the robots could eventually allow for a new type of indirect landscaping artistry. Some former workers, or others, might create inventive approaches-holographic topiary, say, that looks different from different angles-that find their way into the tree-trimming models. With data dignity, the models might create new sources of income, distributed through collective organizations. Tree trimming would become more multifunctional and interesting over time; there would be a community motivated to remain valuable. Each new successful introduction of an A.I. or robotic application could involve the inauguration of a new kind of creative work. In ways large and small, this could help ease the transition to an economy into which models are integrated.

Many people in Silicon Valley see universal basic income as a solution to potential economic problems created by A.I. But U.B.I. amounts to putting everyone on the dole in order to preserve the idea of black-box artificial intelligence. This is a scary idea, I think, in part because bad actors will want to seize the centers of power in a universal welfare system, as in every communist experiment. I doubt that data dignity could ever grow enough to sustain all of society, but I doubt that any social or economic principle will ever be complete. Whenever possible, the goal should be to at least establish a new creative class instead of a new dependent class.

There are also non-altruistic reasons for A.I. companies to embrace data dignity. The models are only as good as their inputs. It's only through a system like data dignity that we can expand the models into new frontiers. Right now, it's much easier to get an L.L.M. to write an essay than it is to ask the program to generate an interactive virtual-reality world, because there are very few virtual worlds in existence. Why not solve that problem by giving people who add more virtual worlds a chance for prestige and income?

Could data dignity help with any of the human-annihilation scenarios? A big model could make us incompetent, or confuse us so much that our society goes collectively off the rails; a powerful, malevolent person could use A.I. to do us all great harm; and some people also think that the model itself could "jailbreak," taking control of our machines or weapons and using them against us.

We can find precedents for some of these scenarios not just in science fiction but in more ordinary market and technology failures. An example is the 2019 catastrophe related to Boeing's 737 MAX jets. The planes included a flight-path-correction feature that in some cases fought the pilots, causing two mass-casualty crashes. The problem was not the technology in isolation but the way that it was integrated into the sales cycle, training sessions, user interface, and documentation. Pilots thought that they were doing the right thing by trying to counteract the system in certain circumstances, but they were doing exactly the wrong thing, and they had no way of knowing. Boeing failed to communicate clearly about how the technology worked, and the resulting confusion led to disaster.

Anything engineered-cars, bridges, buildings-can cause harm to people, and yet we have built a civilization on engineering. It's by increasing and broadening human awareness, responsibility, and participation that we can make automation safe; conversely, if we treat our inventions as occult objects, we can hardly be good engineers. Seeing A.I. as a form of social collaboration is more actionable: it gives us access to the engine room, which is made of people.

Let's consider the apocalyptic scenario in which A.I. drives our society off the rails. One way this could happen is through deepfakes. Suppose that an evil person, perhaps working in an opposing government on a war footing, decides to stoke mass panic by sending all of us convincing videos of our loved ones being tortured or abducted from our homes. (The data necessary to create such videos are, in many cases, easy to obtain through social media or other channels.) Chaos would ensue, even if it soon became clear that the videos were faked. How could we prevent such a scenario? The answer is obvious: digital information must have context. Any collection of bits needs a history. When you lose context, you lose control.

Why don't bits come attached to the stories of their origins? There are many reasons. The original design of the Web didn't keep track of where bits came from, likely to make it easier for the network to grow quickly. (Computers and bandwidth were poor in the beginning.) Why didn't we start remembering where bits came from when it became more feasible to at least approximate digital provenance? It always felt to me that we wanted the Web to be more mysterious than it needed to be. Whatever the reason, the Web was made to remember everything while forgetting its context.

Today, most people take it for granted that the Web, and indeed the Internet it is built on, is, by its nature, anti-contextual and devoid of provenance. We assume that decontextualization is intrinsic to the very idea of a digital network. That was never so, however; the initial proposals for digital-network architecture, put forward by the monumental scientist Vannevar Bush in 1945 and the computer scientist Ted Nelson in 1960, preserved provenance. Now A.I. is revealing the true costs of ignoring this approach. Without provenance, we have no way of controlling our A.I.s, or of making them economically fair. And this risks pushing our society to the brink.

If a chatbot appears to be manipulative, mean, weird, or deceptive, what kind of answer do we want when we ask why? Revealing the indispensable antecedent examples from which the bot learned its behavior would provide an explanation: we'd learn that it drew on a particular work of fan fiction, say, or a soap opera. We could react to that output differently, and adjust the inputs of the model to improve it. Why shouldn't that type of explanation always be available? There may be cases in which provenance shouldn't be revealed, so as to give priority to privacy-but provenance will usually be more beneficial to individuals and society than an exclusive commitment to privacy would be.

The technical challenges of data dignity are real and must inspire serious scientific ambition. The policy challenges would also be substantial-a sign, perhaps, that they are meaningful and concrete. But we need to change the way we think, and to embrace the hard work of renovation. By persisting with the ideas of the past-among them, a fascination with the possibility of an A.I. that lives independently of the people who contribute to it-we risk using our new technologies in ways that make the world worse. If society, economics, culture, technology, or any other spheres of activity are to serve people, that can only be because we decide that people enjoy a special status to be served.

This is my plea to all my colleagues. Think of people. People are the answer to the problems of bits. ♦

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There are ways of controlling the new technology-but first we have to stop mythologizing it. As a computer scientist, I don't like the term "A.I." In fact, I think it's misleading-maybe even a little dangerous. Everybody's already using the term, and it might seem a little late in the day to be arguing about it.

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ChatGPT - Navigating The Impact of Generative AI Technologies on Educational Theory and Practice.

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ChatGPT - Navigating The Impact of Generative AI Technologies on Educational Theory and Practice.

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Over 25 Educators contributed to the chapters in this book on a range of topics from understanding Large Language Models and the algorithms that drive generative AI like ChatGPT to practical applications in K-16 classrooms and leadership, to ethical and policy topics.

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Chat(GPT): Navigating the Impact of Generative AI Technologies on Educational Theory and Practice: Educators Discuss ChatGPT and other Artificial Intelligence Tools

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Buy Chat(GPT): Navigating the Impact of Generative AI Technologies on Educational Theory and Practice: Educators Discuss ChatGPT and other Artificial Intelligence Tools: Read Kindle Store Reviews - Amazon.com

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Best Practice Strategies for Effective Use of Questioning As A Teaching Tool 

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Best Practice Strategies for Effective Use of Questioning As A Teaching Tool 

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Abstract

Questions have long been used as a teaching tool by teachers and preceptors to assess students' knowledge, promote comprehension, and stimulate critical thinking. Well-crafted questions lead to new insights, generate discussion, and promote the comprehensive exploration of subject matter. Poorly constructed questions can stifle learning by creating confusion, intimidating students, and limiting creative thinking. Teachers most often ask lower-order, convergent questions that rely on students' factual recall of prior knowledge rather than asking higher-order, divergent questions that promote deep thinking, requiring students to analyze and evaluate concepts. This review summarizes the taxonomy of questions, provides strategies for formulating effective questions, and explores practical considerations to enhance student engagement and promote critical thinking. These concepts can be applied in the classroom and in experiential learning environments.

Keywords: questioning, critical thinking, pedagogy, effective teaching, teaching tool

INTRODUCTION

Using questions to teach is an age-old practice and has been a cornerstone of education for centuries.1,2 Questions are often used to stimulate the recall of prior knowledge, promote comprehension, and build critical-thinking skills. Teachers ask questions to help students uncover what has been learned, to comprehensively explore the subject matter, and to generate discussion and peer-to-peer interaction. Student-initiated questions increase higher-order learning by requiring them to analyze information, connect seemingly disparate concepts, and articulate their thoughts.3 Indeed, questions are ubiquitous, but are the right kinds of questions - ones that promote learning, not recall - asked at the appropriate time? 4 Poor questions can stifle learning by creating confusion, intimidating students, and limiting creative thinking.5 Effective questions asked in a psychologically safe learning environment support student learning by probing for understanding, encouraging creativity, stimulating critical thinking, and enhancing confidence.6

The art of asking the right questions at the appropriate time is not innate.7 Bloom's taxonomy of learning categorizes cognitive levels into several domains.8 Questions that elicit responses in the knowledge, comprehension, and application domains are frequently considered lower-order questions, while questions in the analysis, synthesis, and evaluation domains are considered higher-order questions.9 Higher-order questions elicit deeper and critical thinking; therefore, teachers are encouraged to ask questions in these domains ( Table 1 ).2,10-12 This does not mean that lower-order questions should not be asked. It is appropriate to ask questions to address all cognitive domains as long as the desired learning outcome is kept in mind and a good mix of questions is used during each teaching session.2 Given that the learning objectives in most courses in graduate and professional degree programs are often intended to stimulate high order cognitive processes, one would expect that higher-order questions would prevail during encounters between students and teachers. Unfortunately, observations of classroom-based instructors have repeatedly shown that lower-order questions are far more frequently used.11,13 Perhaps teachers do not value higher-order questions and feel they are not effective, or perhaps a lack of formal training on how to formulate questions to stimulate learning is the root cause.4

Table 1.

There is a paucity of empirical data regarding how to most effectively use questions to teach.1,10,11,13,14 The purpose of this review is to examine the taxonomy of questions and suggest some best practice strategies for formulating questions that can help achieve desired teaching objectives and learning outcomes.12 To effectively illustrate these concepts, the examples provided throughout this manuscript relate to a specific content area: analgesics and pain management.

TAXONOMY OF QUESTIONS

Questions have been classified into several taxonomies intended to describe their fundamental essence. For the purposes of teaching, these taxonomies can be used by educators to formulate questions intended to elicit specific cognitive processes. The basic way to characterize questions is to classify them as either convergent or divergent.1 A convergent question, often called a closed question, is intended to elicit a specific response or a narrow list of possible responses. Educators use convergent questions to draw a single "best" response from learners. In contrast, divergent questions, also known as open questions, elicit a wide range of responses that often require substantive elaboration. Divergent questions do not have a single "best" response. Educators use divergent questions to stimulate dialog and explore a range of issues related to the topic ( Table 1 ).

Another way to classify questions is to examine their cognitive level or complexity. A hierarchal approach to cognition was originally described by Bloom and subsequently modified by Anderson and Krathwohl ( Table 2 ).2,8,15 Questions may address various levels of cognition ranging from mere recall of memorized facts to processes that require deep critical thinking.10 Questions can, therefore, address various cognitive domains with the intent of achieving specific learning outcomes. Each domain is further categorized as lower or higher order in terms of cognitive difficulty.

Table 2.

Remembering, which is the act of recalling information, is considered the lowest order of cognitive processing and yet recall-type questions are the most frequently posed by educators. Questions aimed at eliciting a student's understanding of the material are those that ask the learner to provide examples, classify items, summarize information, and/or draw inferences. Application questions require the learner to execute a procedure or process, mental or physical, to an unfamiliar situation or circumstance. Analysis requires the learner to break down the material into constituent parts and determine the inter-relationships among them. Analysis questions may ask the learner to organize elements within a structure, distinguish relevant from irrelevant information, or deconstruct underlying values and biases. Evaluation requires formulating judgments based on standards or existing criteria. Evaluating questions require the learner to critique a work or product, determine the appropriateness of a process or product for a given problem, or examine the inconsistencies in a theory. Finally, creating is considered the most difficult task in terms of cognitive processing. Questions that address this cognitive domain may require learners to generate alternative hypotheses based on observed phenomena, devise a new procedure to accomplish a task, or conceptualize a new product.

Questions can also be classified into knowledge dimensions. Anderson and Krathwohl describe 4 types of knowledge: factual, conceptual, procedural, and metacognitive.15 These knowledge dimensions range from the concrete to abstract ( Table 3 ). Factual knowledge includes the technical vocabulary used in a discipline and the details extracted from reliable sources of information. While factual questions often ask students to recall specific elements from a reference source, they address higher-order thinking. Questions on the factual dimension of knowledge can be constructed to demonstrate understanding, prompt analysis, or evaluate the work of others. Conceptual knowledge includes an awareness of the inter-relationships between the elements of a larger structure. Conceptual questions might ask learners to justify an answer based on underlying principles or theories, or to classify elements into categories. Procedural knowledge is the ability to use algorithms, techniques, or criteria as well as the ability to determine when it is appropriate to use them. Procedural questions might ask the learner about well-established methods for gathering information or selecting the most appropriate equation in a particular situation. Finally, metacognitive knowledge is an awareness of one's own cognition. Metacognition questions might ask a learner to articulate a cognitive strategy required to complete a task or examine personal motivations and values.15

Table 3.

Teachers predominantly ask lower-level cognitive questions that do not effectively stimulate critical thinking.11,13 During classroom-based instruction, researchers observed 91 faculty members asking 3,407 questions, and categorized the type and level of each question posed.11 The majority of the questions asked were lower-level questions (68.9%). In a similarly designed study, Sellappah and colleagues found that during practice-based experiences, clinical instructors asked lower-level questions 91.2% of the time.13 Further, the clinical instructors' years of experience as a practitioner or teacher were not correlated with their propensity to ask lower- or higher-order questions. A study by Phillips and Duke compared the cognitive level of questions asked by full-time clinical faculty members and volunteer preceptors in nursing programs.14 Both faculty members and preceptors asked lower-order questions far more frequently than higher-order ones. However, full-time faculty members asked higher-order cognitive questions more frequently than preceptors.

Several nonhierarchical types of question prompts, including focal, brainstorm, shotgun, and funnel questions, are widely used in teaching ( Table 1 ).12 Shotgun questions and funnel questions often elicit confusion and generally should be avoided. A study that used a variety of question prompts directed at students enrolled in an online course found that brainstorming questions elicited the highest number of responses per prompt while shotgun questions elicited the least. Questions in the comprehension and application domains of Bloom's taxonomy elicited the highest average number of responses per prompt, whereas questions in the synthesis and comprehension level elicited the highest number of student-to-student exchanges.

STRATEGIES FOR FORMULATING QUESTIONS

Socratic Method Questioning

The Socratic method of questioning is a popular teaching approach.16,17 This method uses a form of inquiry that promotes open discussion in which one viewpoint is compared to another. Instead of providing information to students directly, the lesson is taught through probing and thought-provoking questions in an effort to stimulate original thought from the students. In order to master the Socratic method, instructors must become skillful at developing 3 categories of questions: exploratory, spontaneous, and focused ( Table 4 ).17

Table 4.

Exploratory questioning is used to find out how much students know about the issue under discussion. This type of question may be implemented to introduce a new topic to the audience, review past discussions of a topic, or determine how much students have retained from the previous learning sessions. A broad range of related issues and topics can be evaluated through this method of questioning. To ensure that the conversation is guided in an intellectual manner, it is best to plan exploratory questions and topics in advance.

Spontaneous questioning is best used when students are naturally curious about the topic or when an ongoing discussion slows. Spontaneous questions are used to probe students' thoughts in an effort to get them to explore their beliefs and assumptions. This type of question prompts students to self-correct, rather than be corrected by the instructor, through reflection on the question being asked. Spontaneous questioning can also be used when an important issue is raised, when students are on the edge of a breakthrough in learning, or when discussion requires clarification.

Focused questioning narrows the content down to specific issues on which the teacher would like the audience to reflect. Focused discussion helps to stimulate students intellectually by forcing them to evaluate their thoughts and perspectives. Students are able to experience an ordered dialogue in which they discover and share ideas and insights with regard to the topic.

A noted benefit of the Socratic method is that students often uncover personal knowledge deficits about the subject matter. This can be a difficult realization for students striving for success in the classroom. This is not necessarily a negative consequence and is often an important step in the learning process.18 When students recognize their knowledge gaps, it can spur them to clarify ideas and seek to better understand the concepts.

Question Circles

Question circles is a strategy that leads students from superficial responses to deeper discussions of the subject matter.2 Originally conceptualized as a process to explore students' understanding of written texts, question circles can be easily adapted to discuss a wide range of learning materials (eg, case studies, videos) or experiences (eg, laboratory exercises, advanced practice experiences). Using this model, questions are posed in 3 different domains: subject matter, personal response, and external reality ( Table 5 and Figure 1 ). The subject matter includes the factual, conceptual, and procedural knowledge derived directly from the reading, learning materials, or experience. The personal response is the learner's reaction to, perceptions of, and direct experiences related to the subject matter. The external reality relates to the broad context in which the subject matter exists and its relationship with society at large as well as other disciplines. After the more superficial issues related to the subject matter, personal response, and external realities have been explored, the teacher then formulates questions related to multiple circles. Enriched questions are those that are derived from the juxtaposition of 2 domains and dense questions are intended to explore the inter-relationships among all 3 domains.

Table 5.

Student-generated Questions

Most questioning approaches focus on the teacher asking the questions. However, student-generated questions also can lead to deep learning.19,20 Requiring students to create their own questions can elicit a greater understanding of the course material.19 Rather than continually questioning students regarding the material, Bowker achieved content understanding by making declarative statements intended to elicit questions from students. When there were no definitive answers to the questions asked, students were expected to reflect on all possible answers, known and hypothetical, to gain a deeper knowledge of the material. Using this questioning strategy, students demonstrated greater thought complexity and engagement.

Another strategy is the questioning as thinking (QAT) framework where students are encouraged to generate questions to aid in their exploration or understanding of a subject matter.20 QAT incorporates both questioning and think-aloud learning strategies to support metacognition. Students are expected to independently monitor their learning by asking themselves questions such as, "What are my goals for learning?" and "Does this material make sense to me?" The think-aloud technique encourages students to verbalize their inner thoughts, thinking processes, and decision-making strategies. As students become more proficient in this exercise, they internalize the dialogue. Educators can facilitate QAT by modeling the relationship between questioning and thinking aloud. Using content material, a teacher can prepare an example think-aloud to demonstrate how multiple answers can stem from a question. The teacher should model the types of metacognitive questions that students must ask themselves. Once the QAT strategy has been modeled by the teacher, students are asked to formulate their own questions, answers, and thought processes during classroom discussions.20

PRACTICAL CONSIDERATIONS

Phrasing and Clarity

The phrasing and clarity of words used in formulating a question greatly influences its effectiveness. An ambiguous question is one that is unclear to students and confounds their response.2 Posing compound questions that require multiple responses frequently leads to confusion. Teachers should avoid this practice. Limiting the number of action verbs per question to 1 will simplify the question.8,15 A well-phrased question enables students to effectively understand the nature and scope of the question being posed. Even the most seasoned teacher is guilty of occasionally formulating vague or ambiguous questions. Careful attention to students' facial expressions and body language can provide clues that signal potential confusion. When a desired response is not forthcoming after the first prompt, rephrasing the question or providing clarifying comments may help elucidate the intent of the question for students. In other instances, repeating the question after a sufficient wait time may also be helpful.2

An effective teacher is able to gauge the audience and formulate questions to fit the cognitive level of students as well as formulate followup questions in response to unexpected answers.1,12 It is best to ask a variety of questions that address different cognitive levels and knowledge domains. Repeatedly asking the same type of questions might intimidate students. There are times when higher-order questions are not appropriate without first establishing a strong knowledge foundation. For example, it would be appropriate to ask lower-level questions in an introductory course. In a capstone course, the instructor should quickly proceed to higher-order questions after posing some lower-order questions intended to elicit prior knowledge. The mix of questions should vary depending on the course content and objectives.

Psychological Safety

When students refrain from answering questions despite repetition, rephrasing, and reducing the cognitive level of the questions, the teacher should consider whether the atmosphere of the classroom or other learning setting is perceived as intimidating, hostile, or unwelcoming. A psychologically unsafe environment will prevent students from expressing their thoughts and opinions aloud.2,4 The behavior and demeanor of the teacher greatly influences the perceived psychological safety of the learning environment. Repeatedly interrupting students before they have finished responding; breaking eye contact with the responder; or using an aggressive or condescending tone of voice, facial expressions such as grimacing, and/or alienating body language such as turning away from the responder will erode students' sense of safety and self-worth. Students who do not feel safe or self-confident will not initiate responses to questions, or will provide short or purposefully erroneous answers when called on.2,4 Students should always be reminded that there may be several correct answers to questions and just because the answer did not match the instructor's, does not mean it was an incorrect response unworthy of discussion. The teacher should strive to ensure that each student feels valued. Students should be permitted to provide a full answer to the question without fear of penalty for an incorrect response.2,4 Positive reinforcement from the teacher is important. Behaviors such as maintaining eye contact, nodding, making affirming verbal comments or sounds, or asking a student to continue with additional clarifying remarks are all ways to encourage more participation.2

Sequencing and Balance

Two additional components of effective questioning are sequencing and balance. Sequencing is asking questions in a patterned order with the purpose to elicit meaningful responses from the audience. Balance is asking both convergent and divergent questions from multiple knowledge domains and at varying cognitive levels. For example, some professors like to first ask students lower-order questions to create a psychologically safe environment. After a few simple questions to get students involved and interested, they move toward questions relating to material that is more analytical in nature. Achieving balance also requires paying attention to the frequency at which questions are used relative to the amount of time spent presenting new information, providing examples, and engaging students in other active-learning activities. The excessive use of questions can lead to negative outcomes and student passivity.4 Some teachers in the classroom have asked up to 400 questions in a day. An overuse of questioning may discourage students from participating in discussions as they begin to feel threatened or interrogated. 2

Wait Time

Defined as the lapse of time that occurs between the teacher's question and a student's response, or the time between a teacher's first and second questions to students, wait time influences the quality and quantity of student responses as well as the initiation of subsequent dialog.2 If students do not respond within a relatively brief period of time (often as little as 1 second) after being asked a question, teachers often will attempt to move the conversation forward by repeating the question, rephrasing the question, or calling on students.2 When a teacher uses higher-order questions, ones that require complex cognitive processes, it may be necessary to provide 1 to 2 minutes of wait time before soliciting responses from students.21,22 A series of wait time studies involving both small groups and large classes was conducted.21,23,24 Wait times were manipulated with student participants at various levels of cognitive development, ranging from elementary school through university classrooms, to determine the optimal wait time and potential benefits for both students and teachers. Increasing wait time by a few seconds had several positive effects on the attitudes and behaviors of both students and teachers.21 Longer wait times consistently resulted in longer student responses, an increase in the number of students volunteering to respond, and an increase in the number of followup questions posed by students. Students significantly reduced the frequency of "I don't know" responses and student achievement scores on tests significantly increased. Improvements in teacher behaviors were also observed after they increased wait times. Question quality, flexibility, and variety increased while the number of questions declined. Conversely, too much wait time can be detrimental to student participation. Depending on the nature and cognitive complexity of the question, a wait time of more than 20 seconds may be perceived as threatening and result in equally poor responses.22

Many teachers make the mistake of answering their own questions, and this behavior will quickly become self-defeating. After a relatively short wait time, some teachers attempt to fill the silence by providing their own answer to the question posed. This behavior has several negative consequences. Students are deprived of the opportunity to grapple with the question and formulate their own responses, a process essential for learning. The teacher fails to learn what the students already know and, more importantly, what they do not understand and merits more instruction. Students will quickly adapt to this behavior by simply "waiting out" the teacher. By repeatedly answering his/her own questions, the teacher is indirectly communicating to students that it is acceptable for them to not answer questions because the teacher will eventually do so. The work of learning will have been shifted away from the students. If students are unable to answer a question after a suitable wait time, the teacher should rephrase the question or modify it in order to address a related or supporting concept that is less cognitively difficult.

Think time is a closely related concept to wait time and is another strategy to improve student participation. Think time is a period of uninterrupted silence imposed by the teacher so that all students can process the question and formulate an answer.1 Because some students can process and respond to questions more rapidly than others, imposing a think time during which no responses will be accepted allows all students an opportunity to process their thoughts and formulate an answer.

Pimping Questions

The term pimping refers to asking a series of difficult questions to a trainee or student in an effort to establish respect for the educator's superior knowledge and intellect.25 Pimping questions are typically asked in rapid succession, are unlikely to be answered correctly, and leave most learners feeling demoralized. Many students develop defense mechanisms in response to this questioning strategy. Dodging (answering a question with a question), bluffing, using evasive techniques (food in mouth), and using avoidance techniques (no eye contact) are all ways students attempt to thwart a questioner. Some students may inform the teacher of their discomfort or respond with hostility. For example, some students have been reported to use personal digital assistants to look up information and pimp the instructor in return.25,26 Pimping harms learning because students feel little desire to engage with the material or ask additional questions.

SUMMARY

Teachers in classroom and experiential learning environments frequently use lower-order, recall-type questions, and the overuse of this type of question hampers efforts to promote deeper, higher-order, critical thinking in students.27 Understanding the taxonomy of questions and best practice strategies may help pharmacy educators formulate a wider range of questions that not only stimulate the recall of important factual, conceptual, and procedural knowledge but also requires learners to analyze, evaluate, and create. Greater attention to metacognition and the learner's personal responses through effective questions can lead students to deep insights. The clarity, sequencing, and delivery of questions, and the psychological safety of the learning environment influence student perceptions, motivation, and achievement of desired educational outcomes. Questions are among the most powerful teaching tools and adopting best practices can significantly enhance the quality of instruction.

ACKNOWLEDGEMENTS

The authors thank Thomas Dowling, PharmD, PhD, and Robert Michocki, PharmD, for their critical feedback regarding manuscript revisions in response to peer review comments.

Articles from American Journal of Pharmaceutical Education are provided here courtesy of American Association of Colleges of Pharmacy

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Best Practice Strategies for Effective Use of Questions as a Teaching Tool

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Questions have long been used as a teaching tool by teachers and preceptors to assess students' knowledge, promote comprehension, and stimulate critical thinking. Well-crafted questions lead to new insights, generate discussion, and promote the ...

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AI for education | Life skills | Khan Academy

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Khan Academy believes that AI and Large Language Models hold the potential to transform education. By providing teachers and students resources and tools to design joyful, personalized, interactive, engaging learning experiences that drive deep learning in the months and years ahead, we hope to become a trusted partner in this learning journey.

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Amid widespread anxiety about automation and machines displacing workers, the idea that technological advances aren't necessarily driving us toward a jobless future is good news.

At the same time, "many in our country are failing to thrive in a labor market that generates plenty of jobs but little economic security," MIT professors David Autor and David Mindell and principal research scientist Elisabeth Reynolds write in their new book " The Work of the Future: Building Better Jobs in an Age of Intelligent Machines. "

The authors lay out findings from their work chairing the MIT Task Force on the Work of the Future, which MIT president L. Rafael Reif commissioned in 2018. The task force was charged with understanding the relationships between emerging technologies and work, helping shape realistic expectations of technology, and exploring strategies for a future of shared prosperity. Autor, Mindell, and Reynolds worked with 20 faculty members and 20 graduate students who contributed research.

Beyond looking at labor markets and job growth and how technologies and innovation affect workers, the task force makes several recommendations for how employers, schools, and the government should think about the way forward. These include investing and innovating in skills and training, improving job quality, including modernizing unemployment insurance and labor laws, and enhancing and shaping innovation by increasing federal research and development spending, rebalancing taxes on capital and labor, and applying corporate income taxes equally.

The first step toward preparing for the future is understanding emerging technologies. In the following excerpt, Autor, an economist, Mindell, a professor of aeronautics, and Reynolds, now the special assistant to the president for manufacturing and economic development, look at artificial intelligence, which is at the heart of both concern and excitement about the future of work. Understanding its capabilities and limitations is essential - especially if, as the authors write, "The future of AI is the future of work."

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To address the time to develop and deploy AI and robotic applications, it is worth considering the nature of technological change over time. When people think of new technologies, they often think of Moore's Law, the apparently miraculous doubling of power of microprocessors, or phenomena like the astonishing proliferation of smartphones and apps in the past decades, and their profound social implications. It has become common practice among techno-pundits to describe these changes as "accelerating," though with little agreement on the measures.

But when researchers look at historical patterns, they often find long gestation periods before these apparent accelerations, often three or four decades. Interchangeable parts production enabled the massive gun manufacturing of the Civil War, for example, but it was the culmination of four decades of development and experimentation. After that war, four more decades would pass before those manufacturing techniques matured to enable the innovations of assembly-line production. The Wright Brothers first flew in 1903, but despite the military application of World War I, it was the 1930s before aviation saw the beginnings of profitable commercial transport, and another few decades before aviation matured to the point that ordinary people could fly regularly and safely. Moreover, the expected natural evolution toward supersonic passenger flight hardly materialized, while the technology evolved toward automation, efficiency, and safety at subsonic speeds - dramatic progress, but along other axes than the raw measure of speed.

The ability to adapt to novel situations is still a challenge for AI and robotics and a key reason why companies continue to rely on human workers for a variety of tasks.
Autor, Mindell, and Reynolds Authors, "Work of the Future"
More recently, the basic technologies of the internet began in the 1960s and 1970s, then exploded into the commercial world in the mid-1990s. Even so, it is only in the past decade that most businesses have truly embraced networked computing as a transformation of their businesses and processes. Task Force member Erik Brynjolfsson calls this phenomenon a "J-curve," suggesting that the path of technological acceptance is slow and incremental at first, then accelerates to break through into broad acceptance, at least for general-purpose technologies like computing. A timeline of this sort reflects a combination of perfecting and maturing new technologies, the costs of integration and managerial adoption, and then fundamental transformations.

While approximate, four decades is a useful time period to keep in mind as we evaluate the relationship of technological change to the future of work. As the science fiction writer William Gibson famously said, "The future is already here, it's just not evenly distributed." Gibson's idea profoundly links the slow evolution of mass adoption to what we see in the world today. Rather than simply making predictions, with their inevitable bias and poor results, we can look for places in today's world that are leading technological change and extrapolate to broader adoption. Today's automated warehouses likely offer a good glimpse of the future, though they will take time for widespread adoption (and likely will not be representative of all warehouses). The same can be said for today's most automated manufacturing lines, and for the advanced production of high-value parts. Autonomous cars are already 15 years into their development cycle but just beginning to achieve initial deployment. We can look at those initial deployments for clues about their likely adoption at scale. Therefore, rather than do research on the future, the task force took a rigorous, empirical look at technology and work today to make some educated extrapolations.

AI today, and the general intelligence of work

Most of the AI systems deployed today, while novel and impressive, still fall into the category of what task force member, AI pioneer, and director of MIT's Computer Science and Artificial Intelligence Laboratory Daniela Rus calls "specialized AI." That is, they are systems that can solve a limited number of specific problems. They look at vast amounts of data, extract patterns, and make predictions to guide future actions. " Narrow AI solutions exist for a wide range of specific problems," write Rus, MIT Sloan School professor Thomas Malone,and Robert Laubacher of the MIT Center for Collective Intelligence, "and can do a lot to improve efficiency and productivity within the work world." Such systems include IBM's Watson system, which beat human players on the American TV game show "Jeopardy! " and its descendants in health care, or Google's AlphaGo program, which also bests human players in the game of Go. The systems we explore in insurance and health care all belong to this class of narrow AI, though they vary in different classes of machine learning, computer vision, natural language processing, or others. Other systems in use today also include more traditional "classic AI" systems, which represent and reason about the world with formalized logic. AI is no single thing but rather a variety of different AIs, in the plural, each with different characteristics, that do not necessarily replicate human intelligence.

Specialized AI systems, through their reliance on largely human-generated data, excel at producing behaviors that mimic human behavior on well-known tasks. They also incorporate human biases. They still have problems with robustness, the ability to perform consistently under changing circumstances (including intentionally introduced noise in the data), and trust, the human belief that an assigned task will be performed correctly every single time. "Because of their lack of robustness," write Malone, Rus, and Laubacher, "many deep neural nets work 'most of the time' which is not acceptable in critical applications." The trust problem is exacerbated by the problem of explainability because today's specialized AI systems are not able to reveal to humans how they reach decisions.

The ability to adapt to entirely novel situations is still an enormous challenge for AI and robotics and a key reason why companies continue to rely on human workers for a variety of tasks. Humans still excel at social interaction, unpredictable physical skills, common sense, and, of course, general intelligence.

From a work perspective, specialized AI systems tend to be task-oriented; that is, they execute limited sets of tasks, more than the full set of activities constituting an occupation. Still, all occupations have some exposure. For example, reading radiographs is a key part of radiologists' jobs, but just one of the dozens of tasks they perform. AI in this case can allow doctors to spend more time on other tasks, such as conducting physical examinations or developing customized treatment plans. In aviation, humans have long relied on automatic pilots to augment their manual control of the plane; these systems have become so sophisticated at automating major phases of flight, however, that pilots can lose their manual touch for the controls, leading in extreme cases to fatal accidents. AI systems have not yet been certified to fly commercial aircraft.

Artificial general intelligence, the idea of a truly artificial human-like brain, remains a topic of deep research interest but a goal that experts agree is far in the future. A current point of debate around AGI highlights its relevance for work. MIT professor emeritus, robotics pioneer, and Task Force Research Advisory Board member Rodney Brooks argues that the traditional "Turing test" for AI should be updated. The old standard was a computer behind a wall with which a human could hold a textual conversation and find it indistinguishable from another person. This goal was achieved long ago with simple chatbots, which few argue represent AGI.

In a world of robotics, as the digital world increasingly mixes with the physical world, Brooks argues for a new standard for AGI: the ability to do complex work tasks that require other types of interaction with the world. One example might be the work of a home health aide. These tasks include providing physical assistance to a fragile human, observing their behavior, and communicating with family and doctors. Brooks' idea, whether embodied in this particular job, a warehouse worker's job, or other kinds of work, captures the sense that today's intelligence challenges are problems of physical dexterity, social interaction, and judgment as much as they are of symbolic data processing. These dimensions remain out of reach for current AI, which has significant implications for work. Pushing Brooks' idea further, we might say that the future of AI is the future of work.

Excerpted from The Work of the Future: Building Better Jobs in an Age of Intelligent Machines by David Autor, David A. Mindell and Elisabeth B. Reynolds. Reprinted with permission from the MIT PRESS. Copyright 2022.
Read next: Machine learning, explained

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Why 'the future of AI is the future of work' | MIT Sloan

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In a new book about how technology will affect workers, MIT experts explain how AI is far from replacing humans - but still changing occupations.

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A robust body of research tells us that flexibility and accuracy with fractions are among the most critical elements for student success in later mathematics. One of the most important jobs of middle-grade elementary teachers is to support all children to build on their understanding of whole numbers and conceptualize fractions as part of the number system to fully reason, compute, and apply fraction understanding.

On this page, there is something for everyone-the classroom teacher, the coach, the instructional supervisor, and the principal-who needs great resources and support for their own learning of fractions as well as the teaching and learning of fractions with students. Use the navigation menu to find research and rationales, examples of fraction problems to use with students, and professional learning resources to build community and learning with colleagues.

Why Fractions?

The teaching and learning of fraction concepts is often quite complex. Many teachers were not themselves taught the critical mathematical concepts underlying fractions when they were students. (Remember the tricks?) Even in their professional training, many teachers have not had opportunities to engage in deep learning dedicated solely to fractions. Although understanding and mastery of fractions have critical implications for mathematical proficiency, these concepts and skills are not consistently prioritized in teacher preparation or professional development.

Further, the world around us bombards our students with images and language about fractions that work against our efforts. Have you ever heard someone say, when splitting a treat, "I want the bigger half!"? By definition, however, halves are equal. Imagine how confusing that language is for students beginning to build fraction understanding. They see drawings of fruit pies and pizza pies and are asked repeatedly to shade in parts of these pies. Does this help them see that a fraction is a number in and of itself, or does it reinforce the incorrect belief that a fraction is only a piece of a number, just a numeric slice of a whole pie?

Many students struggle with fractions, but students of color, students from families experiencing poverty, and English learners are less likely to have access to effective instructional practices, appropriate individualized supports, or teaching practices and materials that reflect their identities. To implement the study of fractions more equitably, we must consider student supports, culturally relevant content and practices, and aligned instructional materials. This means that educators must have a deep knowledge of fraction content and instruct in ways that value all students' backgrounds, languages, cultures, points of view, knowledge, and skills. You'll find these essential elements of teaching fractions throughout this collection.

Supporting Social, Emotional, and Academic Development in Mathematics. It is clear that not only must attention be spent on fraction understanding and students' academic development but we must also attend to their social and emotional needs. In this document, you will find a more detailed description of how to do this, including sample actions to support student agency, identity, and belonging.

Coherence Map. The Coherance Map was incredibly useful in the design of this fraction learning experience. Here is a brief video, created by the Washington Core Advocates, that highlights the Coherence Map as a tool.

Coherence Map Fraction Activity. Use this activity to look at standards across grade levels. Within each standard, spend some time reading the focus standard, excerpts from the Progression document relevant to that standard, and tasks that fall within the standard. Consider: what are the connections between standards and the downstream implications of students understanding fraction value based on unit thinking?

Fraction Guidance for Educators. Teaching of fraction content across individual classrooms and schools can vary dramatically. If your students seem to stumble when working with fractions, they might be missing some foundational fractions concepts. This guidance document is designed to help you identify some traditional practices that may be impeding student learning, and to provide ideas for alternate approaches.

Fractions as Numbers

If you pose the question, " Are fractions numbers, and how do you know? " you might be surprised by the responses and rationales you get. The misconceptions you will find in these answers are indicative of what makes both the understanding and teaching of fractions complex. Throughout the following grade-level collections of tasks, you'll find resources to support the thinking behind confident (and accurate) answers.

Yes, a fraction is a number because it may be placed on the number line.

As the story of fractions unfolds, it is important to stay connected to the progression laid out by the standards themselves, dig deeply, and allow for exploration and sensemaking. The unit fraction plays a critical role in this learning and is featured prominently within each grade-level collection. The grade-level collections intentionally highlight the number line over bar models and circular models to emphasize fractions to prior work with whole numbers, and do not include every NF standard or fully address the aspect of rigor in a single grade.

Grade 3 Tasks

How would you describe the length of the segment shown?

This entry task is one of the seven examples from this grade-level collection. It serves as a bridge between students' prior work with whole numbers and the grade 3 introduction to fractions as numbers. It provides an entry point for the work of fractions that attempts to pique students' curiosity toward answering the question, "Why do we need numbers other than whole numbers, anyway?"

In grade 3, the focus is on fractions as numbers and expanding student thinking on the number line to include fractions as well as whole numbers.

Grade 3 Fraction Tasks. This resource uses examples of problems to highlight the big ideas in fractions for grade 3. There are student work samples - sometimes written and sometimes in video, additional resources for instructional support, and questions to promote student identity, agency, and belonging as learners and doers of mathematics.

Grade 4 Tasks

Which fraction is bigger: ³/₇ or ⁶/₁₃?

This entry task shows the uptick from the work of grade 3 - where students compare fractions with like numerators or like denominators, to the work of grade 4 - where students compare fractions with unlike numerators and unlike denominators. This is one of the nine examples you'll find within the grade-level collection.

In grade 4, the focus is on students applying what they understand about the concept of fractions to compare fractions and add and subtract fractions. Additionally, the domain steps it up a notch as students are introduced to decimals and reason about their size in comparison to fractions of tenths and hundredths.

Grade 4 Fraction Tasks. This resource uses examples of problems to highlight the big ideas in fractions for grade 4. There are student work samples - sometimes written and sometimes in video, additional resources for instructional support, and questions to promote student identity, agency, and belonging as learners and doers of mathematics.

Grade 5 Tasks

What is the sum of these fractions?

This entry task serves as a bridge between students' grade 4 skills of generating equivalent fractions and adding fractions with like denominators and the grade 5 expectation of adding fractions and fractions over one with unlike denominators. There is further description of this task, along with seven additional examples, to highlight the content of the grade.

In grade 5, the focus is on students extending their skills with whole number operations - adding, subtracting, multiplying, and dividing - to fractions.

Grade 5 Fraction Tasks. This resource uses examples of problems to highlight the big ideas in fractions for grade 5. There are student work samples - sometimes written and sometimes in video, additional resources for instructional support, and questions to promote student identity, agency, and belonging as learners and doers of mathematics.

Grade 6 and Beyond Tasks

While the Number and Operations - Fractions domain spans grades 3-5, the study of fractions and their applications extends well beyond the elementary grades.

How would you write the numbers at points A and B?

This task is designed to have students leverage their understanding of fractions as numbers developed across grades 3-5 and expand their known number system to now include rational numbers.

Grade 6 and Beyond Fraction Tasks. This resource uses examples of problems to highlight the big ideas in fractions for grade 6 into high school. There are student work samples in written and/or video forms, additional resources for instructional support, and questions to promote student identity, agency, and belonging as learners and doers of mathematics.

Grade 6 Do Nows. As students progress through elementary and middle grades, it is important to bring them into grade-level content in such a way that affirms knowledge they already hold. The Do Now resources are meant to be used at the onset of class to bring students into the grade 6 content of Ratios and Proportional Reasoning. The resources pair a student work page with a teacher's guide that contains instructional questions and language supports as well as annotated student work.

Coaches & Instructional Leaders

Coaches and instructional leaders do important work to support teachers and students engaging with fractions content. The resources below are intended to help ensure high-quality professional learning and coaching.

Question the Image Fraction Activity. Use this professional learning activity to examine and discuss possible images and visuals you and your students may encounter in fraction units of study. Are the fraction images students are presented with supporting their learning or impeding their understanding?

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A robust body of research tells us that flexibility and accuracy with fractions are among the most critical elements for student success in later mathematics. One of the most important jobs of middle-grade elementary teachers is to support all children to build on their understanding of whole numbers and conceptualize fractions as part of the number system to fully reason, compute, and apply fraction understanding.

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Not as user friendly as Dall-e but produces high quality images.

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The QFT process is a way to teach students to ask better questions - which will benefit them in their work in the classroom and with generative AI tools.

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The skill of question asking is far too rarely deliberately taught in school. We have worked with and learned from educators to develop a teaching strategy, the Question Formulation Technique (QFT), which provides a simple yet powerful way to teach students how to formulate, work with, and use their own questions.

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Teaching + Learning - Right Question Institute

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We want everyone to use our tools safely and responsibly. That's why we've created usage policies that apply to all users of OpenAI's models, tools, and services. By following them, you'll ensure that our technology is used for good.

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Artificial general intelligence has the potential to benefit nearly every aspect of our lives-so it must be developed and deployed responsibly.

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Trusted Learning Environment - Student Data Privacy

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Trusted Learning Environment - Student Data Privacy

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To empower people to unlock the joy of discovery, feel the wonder of creation and better harness the world's knowledge, today we're improving how the world benefits from the web by reinventing the tools billions of people use every day, the search engine and the browser.

Today, we're launching an all new, AI-powered Bing search engine and Edge browser, available in preview now at Bing.com, to deliver better search, more complete answers, a new chat experience and the ability to generate content. We think of these tools as an AI copilot for the web.

"AI will fundamentally change every software category, starting with the largest category of all - search," said Satya Nadella, Chairman and CEO, Microsoft. "Today, we're launching Bing and Edge powered by AI copilot and chat, to help people get more from search and the web."

There are 10 billion search queries a day, but we estimate half of them go unanswered. That's because people are using search to do things it wasn't originally designed to do. It's great for finding a website, but for more complex questions or tasks too often it falls short.
The new Bing and Edge - Your copilot for the web
We have brought together search, browsing and chat into one unified experience you can invoke from anywhere on the web, delivering:

Better search. The new Bing gives you an improved version of the familiar search experience, providing more relevant results for simple things like sports scores, stock prices and weather, along with a new sidebar that shows more comprehensive answers if you want them.

Complete answers. Bing reviews results from across the web to find and summarize the answer you're looking for. For example, you can get detailed instructions for how to substitute eggs for another ingredient in a cake you are baking right in that moment, without scrolling through multiple results.

A new chat experience. For more complex searches - such as for planning a detailed trip itinerary or researching what TV to buy - the new Bing offers new, interactive chat. The chat experience empowers you to refine your search until you get the complete answer you are looking for by asking for more details, clarity and ideas - with links available so you can immediately act on your decisions.

A creative spark. There are times when you need more than an answer - you need inspiration. The new Bing can generate the content to help you. It can help you write an email, create a 5-day itinerary for a dream vacation to Hawaii, with links to book your travel and accommodations, prep for a job interview or create a quiz for trivia night. The new Bing also cites all its sources, so you're able to see links to the web content it references.

New Microsoft Edge experience. We've updated the Edge browser with new AI capabilities and a new look, and we've added two new functionalities: Chat and compose. With the Edge Sidebar, you can ask for a summary of a lengthy financial report to get the key takeaways - and then use the chat function to ask for a comparison to a competing company's financials and automatically put it in a table. You can also ask Edge to help you compose content, such as a LinkedIn post, by giving it a few prompts to get you started. After that, you can ask it to help you update the tone, format and length of the post. Edge can understand the web page you're on and adapts accordingly.

With the new Bing, exploring the web isn't just easier, it's also more fun. Ask it things like:
My anniversary is coming up in September, help me plan a trip somewhere fun in Europe, Will the Ikea Klippan loveseat fit into my 2019 Honda Odyssey? I like electronic music and want to go to my first festival this year. Do you have any recommendations or tips for me? Reinventing search with AI leaving from London.
The new Bing experience is a culmination of four technical breakthroughs:
Microsoft and OpenAI - innovating together, responsibly

Next-generation OpenAI model. We're excited to announce the new Bing is running on a new, next-generation OpenAI large language model that is more powerful than ChatGPT and customized specifically for search. It takes key learnings and advancements from ChatGPT and GPT-3.5 - and it is even faster, more accurate and more capable.

Microsoft Prometheus model. We have developed a proprietary way of working with the OpenAI model that allows us to best leverage its power. We call this collection of capabilities and techniques the Prometheus model. This combination gives you more relevant, timely and targeted results, with improved safety.

Applying AI to core search algorithm. We've also applied the AI model to our core Bing search ranking engine, which led to the largest jump in relevance in two decades. With this AI model, even basic search queries are more accurate and more relevant.

New user experience. We're reimagining how you interact with search, browser and chat by pulling them into a unified experience. This will unlock a completely new way to interact with the web.

These groundbreaking new search experiences are possible because Microsoft has committed to building Azure into an AI supercomputer for the world, and OpenAI has used this infrastructure to train the breakthrough models that are now being optimized for Bing.
Live today in limited preview, expanding to millions soon
Together with OpenAI, we've also been intentional in implementing safeguards to defend against harmful content. Our teams are working to address issues such as misinformation and disinformation, content blocking, data safety and preventing the promotion of harmful or discriminatory content in line with our AI principles.

The work we are doing with OpenAI builds on our company's yearslong effort to ensure that our AI systems are responsible by design. We will continue to apply the full strength of our responsible AI ecosystem - including researchers, engineers and policy experts - to develop new approaches to mitigate risk.
Related links:
The new Bing is available today in a limited preview on desktop, and everyone can visit Bing.com today to try sample queries and sign up for the waitlist. We're going to scale the preview to millions in the coming weeks. A mobile experience will also be in preview soon.

We're excited to put the new Bing and Edge into the real world to get the critical feedback required to improve our models as we scale.
Learn more about advertising on the new Bing More information about the announcement
Amy Hood, Microsoft executive vice president and chief financial officer, will host a conference call with investors at 2:30 p.m. PT.

Brad Smith, Microsoft vice chair and president: Meeting the moment: advancing the future through responsible AI

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Reinventing search with a new AI-powered Microsoft Bing and Edge, your copilot for the web - The Official Microsoft Blog

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To empower people to unlock the joy of discovery, feel the wonder of creation and better harness the world's knowledge, today we're improving how the world benefits from the web by reinventing the tools billions of people use every day, the search engine and the browser.

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https://chat.openai.com/chat

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ChatGPT helps you get answers, find inspiration, and be more productive.

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Artificial Intelligence Art Creation

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Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen
Engineering, Design, Product, and Prototyping
Jeff Belgum, Dave Cummings, Jonathan Gordon, Chris Hallacy, Shawn Jain, Joanne Jang, Fraser Kelton, Vishal Kuo, Joel Lehman, Rachel Lim, Bianca Martin, Evan Morikawa, Rajeev Nayak, Glenn Powell, Krijn Rijshouwer, David Schnurr, Maddie Simens, Kenneth Stanley, Felipe Such, Chelsea Voss, Justin Jay Wang
Comms, Policy, Legal, Ops, Safety, and Security
Steven Adler, Lama Ahmad, Miles Brundage, Kevin Button, Che Chang, Fotis Chantzis, Derek Chen, Frances Choi, Steve Dowling, Elie Georges, Shino Jomoto, Aris Konstantinidis, Gretchen Krueger, Andrew Mayne, Pamela Mishkin, Bob Rotsted, Natalie Summers, Dave Willner, Hannah Wong
Acknowledgments
Thanks to those who helped with and provided feedback on this release: Sandhini Agarwal, Sam Altman, Chester Cho, Peter Hoeschele, Jacob Jackson, Jong Wook Kim, Matt Knight, Jason Kwon, Anna Makanju, Katie Mayer, Bob McGrew, Luke Miller, Mira Murati, Adam Nace, Hyeonwoo Noh, Cullen O'Keefe, Long Ouyang, Michael Petrov, Henrique Ponde de Oliveira Pinto, Alec Radford, Girish Sastry, Pranav Shyam, Aravind Srinivas, Ilya Sutskever, Preston Tuggle, Arun Vijayvergiya, Peter Welinder

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DALL·E 2 is an AI system that can create realistic images and art from a description in natural language.

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Generative AI Reveals Deep Seated Systemic Issues Beyond The Tech Industry 

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Generative AI Reveals Deep Seated Systemic Issues Beyond The Tech Industry 

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Generative AI like ChatGPT reveal deep-seated systemic issues beyond the tech industry

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Rather than seeing artificial intelligence as the cause of new problems, we might better understand AI ethics as bringing attention to old ones.

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ChatGPT scrutiny brings STPP Artificial Intelligence study new relevance

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6 Big Ethical Questions About the Future of AI

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6 Big Ethical Questions About the Future of AI

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Ethical AI expert Genevieve Bell has 6 framing questions to think about future technology.

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Get a daily email featuring the latest talk, plus a quick mix of trending content.

By subscribing, you understand and agree that we will store, process and manage your personal information according to our Privacy Policy

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Genevieve Bell: 6 big ethical questions about the future of AI

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Artificial intelligence is all around us ... and the future will only bring more of it. How can we ensure the AI systems we build are responsible, safe and sustainable? Ethical AI expert Genevieve Bell shares six framing questions to broaden our understanding of future technology -- and create the next generation of critical thinkers and doers.

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While not AI integrated YET...this open source tool is being used in education to create interactive 3D immersive environments. Read more about it here.

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The Roblox Community Fund (RCF) is a public benefit LLC with an initial $10 million in funding earmarked for supporting impact initiatives at Roblox. By making grants to leading education organizations and developers, the RCF aspires to help 100 million students learn with Roblox by 2030.

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Learn how you can get started with Roblox to excite, inspire, and engage students in ways that go beyond the traditional classroom experience.

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Manuel Lima: A visual history of human knowledge

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How does knowledge grow? Sometimes it begins with one insight and grows into many branches; other times it grows as a complex and interconnected network. Infographics expert Manuel Lima explores the thousand-year history of mapping data -- from languages to dynasties -- using trees and networks of information.

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Char's 15 Min. Webinar ChatGPT As A Learning Assistant

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Char's 15 Min. Webinar ChatGPT As A Learning Assistant

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This is my overview of how to think about generative AI as a learning assistant.

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How Students Can Use ChatGPT as a Tutor (Char Shryock, Retired Interim Sup and Dir of Curriculum)

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The webinar has been broken into a playlist by topic.  The First 3 segments are about the technology behind large language models like generative AI. ChatGPT is one generative AI tool.

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Webinar Intro -- ChatGPT and Education: Purpose and Assumptions of the Webinar (Priten, Stefan)

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Multiple essays from the staff of Stanford University's Human-Centered Artificial Intelligence Department

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Ethics, Policy, Federal Law, and Student Data Privacy

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Ethics, Policy, Federal Law, and Student Data Privacy