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How AI may reshape career pathways to better jobs | Brookings

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Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives

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Examining survey data from corporate executives, the authors find widespread but uneven AI adoption, positive labor productivity gains varying across sectors and strengthening in 2026, and limited near-term job loss alongside compositional shifts in jobs as a result of AI.

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The AI durability of built environment careers | Brookings

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By looking at AI exposure and complementarity across the built environment, this analysis reveals promising characteristics of these positions to support greater economic opportunity.

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Shaping California’s AI future with workers at the center - The James Irvine Foundation

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Expanding opportunity for the people of California

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To save entry-level jobs from AI, look to the medical residency model

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To save entry-level jobs from AI, look to the medical residency model

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To save entry-level jobs from AI, look to the medical residency model | Brookings

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How might artificial intelligence affect Texas’ good jobs?

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Recent Dallas Fed research emphasizes that AI can either help or replace workers depending on the nuance of their occupations. But how might AI affect jobs that don’t require bachelor’s degrees but still pay self-sustaining wages?

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How to Make Emerging Tech Work for Communities with Disabilities

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How to Make Emerging Tech Work for Communities with Disabilities

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Kéah Sharma, Carrie Gillispie, and An-Me Chung

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How AI and Other Technology Can Improve the Lives of People with Disabilities

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Technology can be both a lifeline and a barrier. Users with disabilities explain how accessible design transforms tech from an obstacle to a tool.

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Has Generative AI Adoption Impacted Labor Demand in the Federal Reserve's Third District?

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Has Generative AI Adoption Impacted Labor Demand in the Federal Reserve's Third District?

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Measuring US workers' capacity to adapt to AI-driven job displacement

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Measuring US workers' capacity to adapt to AI-driven job displacement

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Measuring US workers’ capacity to adapt to AI-driven job displacement | Brookings

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There is both broad resilience and concentrated pockets of potential vulnerability in the U.S. labor market when it comes to AI job displacement.

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Implications and Strategies for Preparing Older Adults for AI in the Workplace

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Implications and Strategies for Preparing Older Adults for AI in the Workplace

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Amanda Briggs, Barbara Butrica, Hailey D’Elia

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This brief describes how artificial intelligence (AI) is changing the workplace for older workers, the skills and training older workers will need to be succ…

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Crossing the Great AI Divide - CEW Georgetown

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For months, the news cycle focused on the plight of recent college graduates and growing fears that entry-level workers are increasingly unable to compete with AI in today’s job market.

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Occupational Exposure to Generative AI in the Third Federal Reserve District

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Occupational Exposure to Generative AI in the Third Federal Reserve District

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The Iceberg Index: Measuring Skills-centered Exposure in the AI Economy

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The Iceberg Index: Measuring Skills-centered Exposure in the AI Economy

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Three recommendations:

1. Center workers in developing solutions.

2. Hold companies accountable for the funds they’ve committed to address AI education and training.

3. Think expansively about a robust policy response—everything from increasing worker power and the right to collective bargaining to expanding a durable and sustainable social safety net to addressing the use of fissuring and related labor arrangements, and more, needs to be on the table—rather than an overemphasis on AI skills training.

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AI & Workforce Development - November 2025 Report - TechEquity Collaborative

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AI is changing the labor landscape. TechEquity’s explores how we can approach workforce development in the modern tech economy.

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Tech is making workers’ jobs more dangerous instead of safer

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Tech is making workers’ jobs more dangerous instead of safer

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To prepare young people for the AI workplace, focus on the fundamentals

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To prepare young people for the AI workplace, focus on the fundamentals

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To prepare young people for the AI workplace, focus on the fundamentals | Brookings

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In an age of AI anxiety, we need collaborative, adaptive responses rather than rigid, single-program solutions.

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Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence

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Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence

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In the Face-Off Against AI, Funders Need to Have Workers' Backs

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In the Face-Off Against AI, Funders Need to Have Workers' Backs

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In the Face-Off Against AI, Funders Need to Have Workers’ Backs

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As artificial intelligence transforms the workplace, philanthropy must act before employees are locked out of the conversation.

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An AI Roadmap for Philanthropy in 2025 | The Center for Effective Philanthropy

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The Technology Association of Grantmakers offers data on philanthropy’s current use of AI and a responsible course for AI in 2025 and beyond.

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2024 State of Philanthropy Tech Survey - Technology Association of Grantmakers

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Every two years, TAG conducts a survey to understand the policies, practices, and perceptions of technology in the grantmaking community.

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Responsible AI Adoption Framework in Philanthropy

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Responsible AI Adoption Framework in Philanthropy

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AI - Technology Association of Grantmakers

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Discover how the “Responsible AI Adoption in Philanthropy” guide equips grantmakers with a holistic framework to integrate AI responsibly.

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Philanthropy has long played an important role in providing initial funding for critical research, advocacy, and infrastructure to serve the greater public good. In 1956, the Rockefeller Foundation funded a five-week gathering of scientists and researchers called the Dartmouth Summer Research Project on Artificial Intelligence. Now recognized as the birthplace of the modern notion of "artificial intelligence," that single grant may be the most consequential in the development of artificial intelligence (AI) (Rockefeller Archive Center, 2022).

It's been seventy years since, and AI is now here to stay. Philanthropy cannot afford to be a passive observer but rather has a critical role in shaping the future of AI.

As governments around the globe struggle to keep pace with the advancements of AI, and create the necessary guardrails to address its risks, grantmakers have a unique obligation and a once-in-a-century opportunity to seize the moment and ensure AI serves the public good, advances equity, and strengthens the nonprofit sector rather than exacerbate existing disparities.

The Obligations and Opportunity for Grantmakers

Philanthropy's goals are driven by a vision for greater justice and opportunity for all. The sector should use its influence and resources to steward ethical solutions, provide critical capital for community-informed innovations, and harness greater impact promised by AI-and funders must push for an AI sector that aligns with the greater social good.

"Philanthropy has a once-in-a-generation opportunity to push AI to fulfill its potential to deliver enormous benefits to society..."

By prioritizing ethical AI, building nonprofit capacity, and leveraging technology for impact, grantmakers can lead the charge in ensuring AI serves humanity-all of humanity, not just those that stand to win the race for market dominance.

Here are 10 considerations and examples from the field about how philanthropic organizations can take proactive steps to address the obligations and opportunities for grantmakers at this critical time:

Funders should support nonprofit organizations that are working on algorithmic fairness, data ethics, and digital rights to ensure that AI systems don't undermine common goals for justice and fairness. One such example comes from the Rockefeller Foundation, which has committed resources to combat bias in AI systems, including a $300,000 grant to Black in AI, a technology research organization addressing issues of discrimination toward people of color in AI facial recognition technology.

Launched in 2017, as a joint project with the MIT Media Lab and the Berkman Klein Center for Internet & Society at Harvard University, the Ethics and Governance of AI Initiative is a hybrid research effort and philanthropic fund that seeks to ensure that technologies of automation and machine learning are researched, developed, and deployed in a way that vindicate social values of fairness, human autonomy, and justice.

Tools like the Technology Association of Grantmakers' guide Responsible AI Adoption in Philanthropy can help organizations assess the ethical and equitable impact of AI initiatives. By adopting equity-centering frameworks for AI exploration, funders can ensure that equity considerations remain front and center in AI projects.

Grantmakers should also allocate funding to help nonprofits leverage AI for greater impact, ensuring they have access to tools and expertise that enhance their mission-driven work.

"AI is here to stay, and philanthropy cannot afford to be a passive observer."

Fund Advocacy Efforts for Value-Aligned Guardrails
Ensuring AI development aligns with ethical principles requires strong advocacy. Funders should support policy efforts that promote accountability, transparency, and justice in AI governance.

Fund and Create Private or Public, Nonprofit LLMs for the Sector
Large language models (LLMs), tailored for nonprofit needs, can promote ethical AI use and prevent the overreliance on commercial AI tools. Philanthropy can lead the way in developing and sustaining such models.

Enhance Philanthropic Decision-Making
AI-powered analytics can help grantmakers make more data-driven, equitable funding decisions. From identifying funding gaps to predicting social impact, AI offers new ways to optimize resource allocation. Investing in ethical AI tools for philanthropy can improve transparency and effectiveness.

"Funders should support nonprofit organizations that are working on algorithmic fairness, data ethics, and digital rights..."

Philanthropy's Opportunity in AI

These are but a few examples that illustrate how grantmakers can meet the moment we are in. As grantmakers seek to address society's biggest challenges against the fast-evolving AI landscape, it is more important than ever to consider funding and advancing initiatives that align with their values.

As funders, we must also not forget our nonprofit partners. By fostering ethical AI development and utilization across the nonprofit sector, we may harness the promise of AI and navigate the perils or pitfalls posed by those organizations that lag behind the curve.

Philanthropy has a once-in-a-generation opportunity to push AI to fulfill its potential to deliver enormous benefits to society-will we answer the call?

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How Philanthropy Can Lead in Building Ethical AI for the Public Good - Non Profit News | Nonprofit Quarterly

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Philanthropy must ensure AI development and deployment advance equity, ethics, and the nonprofit sector's ability to serve the public good.

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How Artificial Intelligence Can Help Develop Education and Employment Assessments

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Leading Companies Launch Consortium to Address AI’s Impact on the Technology Workforce

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Cisco (NASDAQ: CSCO) and a group of eight leading companies including Accenture, Eightfold, Google, IBM, Indeed, Intel, Microsoft and SAP as well as six advisors today announced the launch of the AI-Enabled Information and Communication Technology (ICT) Workforce Consortium focused on upskilling and reskilling roles most likely to be impacted by AI. The Consortium is catalyzed by the work of the U.S.-EU Trade and Technology Council’s (TTC) Talent for Growth Task Force, with the goal of exploring AI’s impact on ICT job roles, enabling workers to find and access relevant training programs, and connecting businesses to skilled and job-ready workers. Working as a private sector collaborative, the Consortium is evaluating how AI is changing the jobs and skills workers need to be successful. The first phase of work will culminate in a report with actionable insights for business leaders and workers. Further details will be shared in the coming months. Findings will be intended to offer

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Elyse Ashburn, Margaret Moffett, and Paul Fain

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A deep dive on emerging trends in AI and economic opportunity.

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The views expressed in this article are the author's own and do not reflect those of any affiliated organization or otherwise.
Without intentional, ethical oversight, the data and algorithms behind AI risk repeating patterns of exclusion, discrimination, and bias.Imagine artificial intelligence (AI) as a powerful force, sweeping through our world like an unseen wind, reshaping industries, and influencing lives in ways both visible and hidden. It reaches into healthcare, finance, justice, education, and public policy, promising to streamline and elevate. But with each step it takes, AI can also carry the heavy weight of past injustices, embedding historical inequities into the future it is helping to build.

For communities of color already marginalized by these systems, AI's potential for progress is shadowed by the risk of repeating harm. Without intentional, ethical oversight, the data and algorithms behind AI risk repeating patterns of exclusion, discrimination, and bias.

Nonprofit leaders are the voices that can hold AI accountable, making it a tool that heals rather than harms, that liberates rather than limits. This endeavor demands bold vision, fearless commitment, and empathy-so that AI transforms into a tool for empowerment rather than a quiet force perpetuating inequity.

Recognizing AI's Silent Harms

AI systems are increasingly making decisions that affect people's lives, often without their knowledge. Consider the decisions made by an AI hiring tool Amazon used in 2015 that screened out female applicants, or the data patterns of ChatGPT 3.5 that favored names on resumes associated with White identities over those associated with Black identities.

Facial recognition technology...struggles to accurately identify Black and Brown faces.

These aren't mere technical quirks; they are echoes of a history where access, opportunity, and dignity were often reserved for some while denied to others. Nonprofit leaders dedicated to social justice know that AI's power to shape lives will further entrench the biases we've fought for generations to dismantle if left unchallenged. This is not only a technical challenge but a profound ethical one, calling for those with the courage to speak for those silenced by design.

Today, AI shapes who receives a loan, who gets a job interview, and who is marked for police surveillance. When AI relies on historical data without scrutiny, it absorbs and amplifies the very biases that have marred the past, placing BIPOC communities at higher risk of harm.

Take facial recognition technology, for instance. Developed using data that rarely represent humans' full diversity, this technology struggles to accurately identify Black and Brown faces. The consequences are severe: wrongful arrests, reinforced racial profiling, and unjust surveillance of already vulnerable communities. For individuals subjected to this flawed technology, these errors aren't simply mistakes; they are evidence that their safety and humanity have been overlooked. In this context, nonprofit leaders are more than advocates-they are guardians of fairness, charged with protecting communities from the harms of biased technology.

In finance, AI algorithms present yet another barrier, making decisions that affect financial opportunities, creditworthiness, and generational wealth. Imagine entire neighborhoods, once redlined and systematically denied opportunity, now being neglected again by algorithms that equate zip codes with financial risk. This isn't just a denial of financial services; it's a denial of possibility, locking communities into cycles of poverty and keeping doors closed to upward mobility. Nonprofits serving these communities are vital allies in highlighting and challenging the often hidden biases that prevent progress.

Nonprofits can ensure that community leaders, activists, and end users are involved throughout, aligning AI solutions with real needs and aspirations.

Carving a Path Forward: Using AI for Equity, Not Entrenchment

Despite these risks, AI holds extraordinary potential to serve as a tool for justice if wielded with care, integrity, and vision. Imagine an AI that doesn't blindly replicate patterns of exclusion but instead reveals them-an AI that illuminates inequities, empowers advocacy, and supports more targeted interventions. It can become a tool that "listens" to communities; recognizes the places where health, housing, and financial services are lacking; and helps organizations address these needs.

An AI that serves all people equitably must be rooted in diverse, representative data reflecting the real lives of all communities, not just a privileged few. Moreover, the journey of building AI must include the perspectives of those directly affected by it, grounding it in empathy and awareness. Here, nonprofit leaders can be the bridge, ensuring that technology amplifies marginalized voices and fosters innovation that truly serves.

Transforming AI into a force for equity requires action -not just awareness. Nonprofit leaders can lead this transformation through the following strategies:

Diverse, Inclusive Data: AI's fairness is only as robust as the data it learns from. Nonprofits must advocate for data that reflect the full diversity of the communities they serve, breaking the narrow perspectives that often skew AI's "learning." This work calls for courage-to demand that marginalized experiences, histories, and identities are not only included but valued. With diverse data, AI can begin making fairer decisions that truly reflect the needs of all communities.

Transparency and Accountability: Trust in AI hinges on transparency. Nonprofits can champion processes to audit AI systems, demanding developers disclose training data sources and algorithms. Communities deserve the power to question and understand the decisions that affect their lives. This transparency isn't a luxury; it's a right and a safeguard against the unseen harm of unchecked technology.

Community-Centered Development: For AI to serve its intended communities, their voices must shape it at every stage. Nonprofits can ensure that community leaders, activists, and end users are involved throughout, aligning AI solutions with real needs and aspirations. These communities' insights and lived experiences are not only relevant-they're essential.

Ethics Review Boards: Companies developing AI should include ethics boards staffed with diverse perspectives, such as racial justice advocates and ethicists. Nonprofits can advocate for these review boards, providing the oversight needed to address potential biases and societal impacts. Modeled after academic Institutional Review Boards, these boards serve as a checkpoint, preventing discriminatory outcomes and prioritizing accountability over profit.

Robust Regulatory Frameworks: Nonprofit leaders are critical in shaping AI policy, championing frameworks that set ethical standards and mandate regular bias audits, fair-practice certifications, and consequences for discriminatory outcomes. These policies, crafted with input from affected communities, can protect vulnerable populations and ensure AI serves everyone fairly.

Immediate Steps for Nonprofit Leaders Committed to Justice

Here are immediate steps nonprofit leaders can take to help mitigate AI biases and ensure technology aligns with their missions for social equity:

Educate and Train Staff: Raise awareness within your organization, equipping staff to recognize and address bias in the tools they use.

Collaborate with AI Ethics Experts: Partner with organizations specializing in AI ethics to audit and enhance AI tools in nonprofit operations, ensuring they uphold ethical standards. Become a member of Algorithmic Justice League or AI4ALL, both of which work on policy reforms related to AI fairness and transparency.

Advocate for Inclusive AI Policies: Engage in policy discussions that prioritize equity in AI, safeguarding fair and just development.

Implement Bias Audits: Regularly review the AI systems in use, identifying and mitigating biases to ensure fair treatment for all communities served. Use IBM's AI Fairness 360 Toolkit, an open-source tool that evaluates models for bias across various use cases and provides insights on how to address those biases.

These actions equip nonprofit leaders to confront AI biases with purpose, opening pathways toward genuine advancements in fairness and inclusivity. By taking these steps, nonprofits not only shield communities from potential harm but also help lay the foundation for a technology landscape that reflects and respects all voices.

A Vision for an Equitable AI Future

As AI becomes embedded in the fabric of society, the stakes for marginalized communities are profound. Without careful guidance, algorithmic systems risk perpetuating the very inequities they should address. But with a commitment to transparency, accountability, and justice, nonprofit leaders can ensure that AI becomes a tool for positive change.

With this vision, AI can reflect our best ideals, striving toward a future where technology amplifies, rather than erases, the experiences of all. By embracing this call to lead, nonprofit leaders can create a future where AI prioritizes justice, honors humanity, and serves as a beacon for inclusivity. This is a journey of courage, resilience, and hope-a path forward in which AI advances not only technology but the rights and dignity of all communities.

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AI and Racial Justice: Navigating the Dual Impact on Marginalized Communities - Non Profit News | Nonprofit Quarterly

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The growing influence of artificial intelligence (AI) risks perpetuating historical injustice and harming marginalized communities. However, nonprofit leaders can be vital in transforming AI into a tool for justice and empowerment.

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Jobs for the Future’s innovation lab, JFFLabs, collaborated with Intel Corporation, to offer three new resources to anticipate coming shifts across industries and occupations, and help workers, institutions, and ecosystems prepare.

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A Practical Guide - Funding AI, ML, & Data for Good

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Your go-to source for responsible AI adoption in the nonprofit sector. No matter where you’re located or what your nonprofit’s mission is, AI has likely come up in staff meetings, board discussions, or conversations with funders and partners. You’ve probably read headlines about AI’s promises and pitfalls or seen nonprofits rush to adopt AI, only… Continue reading AI For Nonprofits Resource Hub

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Whether you need help performing a task outside of your usual skill set or you're just looking to streamline your workflow and save time, AI tools may be able to help. - Getty Images/Nastasic

More and more small businesses have adopted artificial intelligence (AI) to streamline their content generation processes, from drafting social media captions to editing photos and videos. While ChatGPT is arguably the most well-known and most commonly used, there's no shortage of AI-powered tools to help you create quality content at scale.

Here are nine other AI tools small businesses can use for content creation.

AI tools for blog and social media writing

Small businesses that leverage content marketing can explore these tools to draft written materials like blogs and social media posts.

Anyword

Anyword is a generative AI platform that can be trained on your brand voice and writing style. One of its primary content generation tools is the Blog Wizard, which can draft blog posts with search engine optimization-friendly keywords and check for plagiarized content. Anyword also offers predictive performance scores for generated content, as well as analytics for live posts.

Lately

Lately is designed specifically to repurpose long-form written content into shorter posts for social media, with the ability to create hundreds of ready-to-go posts in minutes. Its AI content writer can build a writing model based on your brand and its guidelines, as well as data from pretested social media posts on what resonates with your target audience.

Writer

If you're looking for a generative AI option not based on GPT, Writer is a text generator and editing tool that boasts both high accuracy and transparency around its language learning model. Writer comes with prebuilt apps for various written content forms, including blog posts, outbound emails, and recaps. You can also set up a style guide and terms for your brand to ensure consistency across content.
[Read more: How AI is Leveling the Marketing Playing Field Between SMBs and Big Business]

AI tools for graphic design

No matter your level of graphic design experience, these AI-powered tools can help you turn your artistic vision into a reality.

Canva

In addition to its vast library of templates and novice-friendly design tools, Canva now includes AI-powered features for image creation and editing. The text-to-image generator can create images from your text descriptions, offering a variety of art styles to choose from. You can also use the magic edit and magic eraser tools to add, edit, or remove elements from existing images in seconds.

Designs.ai
[Read more: 6 Smart AI Tools to Create Better Graphics and Photos]

Designs.ai comes with a full suite of AI tools for graphic design, capable of creating everything from logos to branded graphics to text-generated images. You can choose from and edit thousands of design templates and variations, color customize free graphics in a variety of illustration styles, or even create a logo and brand kit to apply across projects.

Microsoft Designer

Microsoft Designer has recently expanded to include generative AI design tools (in preview at the time of writing). Available-to-try features include text-to-image and text-to-design creators, generative erase, and a brand kit creator, along with various prompt templates and design templates. Additional functionalities, such as the ability to convert existing pictures into a new visual style, are expected to be released in the near future.

AI tools for audio/video content

If your business uses podcasts, instructional videos, or other audio/video content, these AI tools can simplify the creation and editing process.

[Read more: CO- aims to bring you inspiration from leading respected experts. However, before making any business decision, you should consult a professional who can advise you based on your individual situation. CO-is committed to helping you start, run and grow your small business. Learn more about the benefits of small business membership in the U.S. Chamber of Commerce, here. 9 AI Tools to Improve Your Business's Marketing]
Adobe Premiere Pro

Adobe Premiere Pro, long considered an industry-standard software, has added several AI features to expedite video editing. Key functionalities include text-based editing of video transcripts (helpful for creating rough cuts quickly), audio/video synchronization, autoducking (balancing levels between dialogue and other audio), and automated morph cut abilities for seamless transitions between clips.

Podcastle

You can quickly and easily repurpose blog posts into podcasts, audiobooks, and other audio-based formats with Podcastle. The AI-powered platform includes tools for recording, transcribing, and editing audio, as well as a Revoice tool that creates a digital copy of your voice. It also allows multiple users to leave feedback and make edits in real time.

Vidyo.ai

If your business creates long-form video content, Vidyo.ai can quickly repurpose that content into short-form clips for social media. In addition to generating trimmed versions of your existing video content, Vidyo.ai can automatically add captions, emojis, and video chapters (e.g., for YouTube) to the clips. You can also leverage the platform's preexisting templates for various social media sites and quickly incorporate your own branded elements.

Learn More

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From writing to design and multimedia, these AI tools have got you covered.

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AI at Work: Why GenAI is More Likely to Support Workers Than Replace Them

The Indeed Hiring Lab assessed the ability of GPT-4o, a GenAI model developed by OpenAI, to perform more than 2,800 job skills. Each skill was assessed across three main areas: The ability of GenAI to provide theoretical knowledge related to the skill; The ability of GenAI to solve problems using the skill; and GenAI's determination of the importance of physical presence in utilizing that skill. These three ratings were considered by the model as part of an ultimate assessment of the likelihood that GenAI could replace a human in performing any of those 2,800+ individual skills. You can find a downloadable PDF version of the report here.
Key Points

Out of more than 2,800 work skills assessed by Indeed, none were deemed "very likely" to be replaced by GenAI. Out of five possible outcomes (very unlikely, unlikely, possible, likely, very likely), the majority (68.7%) of skills assessed were "very unlikely" or "unlikely" to be replaced by GenAI.
Roughly a quarter of skills (28.5%) could possibly be replaced by GenAI in the future if businesses change some practices and the tools improve.

Overall, GenAI is generally strong at providing theoretical knowledge of skills but less so at using skills to solve problems. As long as a skill requires significant hands-on execution (for example, "aviation" or "cooking" skills), the usefulness of GenAI will remain limited.

Ultimately, the impact of GenAI on a given occupation will depend on the skills in that occupation that can or cannot be performed well by the technology, both today and in the future.

In jobs with a high share of skills that require hands-on execution, including nursing, GenAI could help with some repetitive tasks (like documentation) and allow workers to refocus on the core skills necessary in these roles.

In more stereotypical "office jobs," including software development, GenAI may potentially be able to offer significant knowledge and solve modest problems, emphasizing the importance of continued upskilling and ongoing learning for human workers.

Despite rapid advances in generative artificial intelligence (GenAI) technology and fears of an impending mass displacement of human workers, human work skills won't be easily replaced in the workforce any time soon. Today's generation of GenAI tools are not "very likely" - literally - to replace a competent human worker in mastering and performing even a single one of thousands of common work skills identified by Indeed.

Every occupation requires a worker to utilize a mix of skills across three key areas: Obtaining, retaining, and providing theoretical knowledge related to those skills; using those skills to solve problems; and executing those skills, either in-person/manually or digitally. A successful human worker can seamlessly balance and adapt their skills across these dimensions in order to do their job. But a digital "worker" powered by GenAI, while potentially strong across one or even two of these measures, will struggle to achieve the same seamless balance across all three.

There is significant potential for these models to continue learning and one day replace humans for certain skills, but meaningful changes in digitalization and working norms will need to happen first. The tools can be an immense help for certain time-consuming tasks, such as summarizing dense texts or quickly generating highly polished images or audio. In the future, combined with advances in robotics, it's possible the tools will become more adept at mimicking and mastering how humans manually apply their skills. But as it stands today, GenAI is best suited to applying its skills to help with relatively straightforward work tasks that require only modest problem solving and, most importantly, no hands-on execution.

An involved approach

Starting with Indeed's universe of hundreds of millions of job postings, we identified more than 2,800 common work skills, from "account management" to "wound care." With this detailed skills taxonomy, Hiring Lab then developed a sophisticated prompt framework to evaluate these skills using GPT-4o, a large language model developed by OpenAI. The model was asked to assess its own ability to perform a skill across the three dimensions noted above. Each of these three assessments was scored on a 5-point scale, with a rating of 1 indicating very limited or no ability to apply a skill across that dimension, and a 5 indicating a strong ability. Finally, ratings across those three dimensions were considered by the model as part of a final assessment asking it to rate the likelihood that GenAI could replace a human across any of those 2,800+ individual skills, on a similar 5-point scale (from 1 - "very unlikely," to 5 - "very likely").

This analysis is the culmination of an intense, months-long, human/AI collaboration that resulted in a highly specific, 1,000+-word prompt to ensure our digital colleague followed our human instructions to the letter. Engineering the prompt took many attempts to get right - the more complex the ask is of GenAI, the more complex and detailed the prompt needs to be. All findings were validated by human researchers, and if the results did not meet expectations, the prompt was adjusted accordingly. Once the prompt was finalized, the analysis was run 15 times and consolidated into a final rating to increase the robustness of the GenAI-derived responses. Ironically, this involved approach helped prove the headline findings from this research: Even as GenAI evolves and learns to complete demanding tasks, humans that oversee, guide, and correct GenAI-derived output will not easily be replaced.

Applied knowledge

Overall, GenAI rated its ability to provide theoretical knowledge somewhat highly, but its ability to solve problems slightly less so. For 79.7% of skills evaluated, GenAI scored itself a 4 ("good") in its ability to provide theoretical knowledge. It said it could solve "moderate" problems (a 3 on the 5-point scale) using 70.7% of skills. Critically, physical execution was deemed "essential" (a 1 on the 5-point scale) or "highly necessary" (a 2 on the 5-point scale) for a small majority (54%) of skills analyzed. Taken together, there were no skills for which GenAI rated itself "very likely" to be able to replace a human worker. More than a quarter of skills (28.5%) could "potentially" be replaced by GenAI as it continues to improve and if certain changes to workplaces and/or working norms occur going forward. But overall, more than two-thirds of workplace skills analyzed (68.7%) were determined to be either "very unlikely" (a 1 on the 5-point scale) or "unlikely" (a 2 on the 5-point scale) to be replaced by GenAI.

Theoretically powerful

Our research shows that the ability to provide theoretical knowledge related to skills is currently GenAI's greatest strength. Theoretical knowledge was the only dimension (aside from physical necessity) in which any of the 2,800+ skills analyzed received the strongest possible rating of 5. The models are "trained" on vast amounts of data over the course of several years, including data from academia, private databases, and other proprietary training information. Even in cases where the models have not been trained on the most recent data, they can often access search engines and look things up. These products can summarize, re-format, and display much of this knowledge in the blink of an eye, and as reference tools, they are likely to only keep improving.

But they are hardly perfect reference tools. While a basic web search today will simply regurgitate what has already been written elsewhere, GenAI is more likely to make new inferences and create new conclusions - sometimes erroneously. The large language models (LLMs) that underpin these tools have a strong theoretical backing in language and writing, but are also prone to leaps and lapses in logic and may make certain things up.

Many GenAI products are also designed to create images or audio that can be impressively realistic and sometimes weird. The data used to train audio/visual-generating tools has often been problematic, leading to images or audio that can reinforce negative racist and/or sexist stereotypes. GenAI also has no real sense of ethics of its own, and cannot know if its responses to a given prompt are morally "right" or "wrong." Human supervision of the prompts that ask for these text, audio, and/or visual outputs - and careful review of the outputs themselves to ensure accuracy, realism, and/or appropriateness - is essential.

Hands-on problems

Beyond providing general knowledge, it is the relative ability of the models to use a skill to solve problems and the importance of physical execution in applying that skill that have the biggest impacts in determining the likelihood of GenAI replacing a human at that given skill. An ability to solve problems is a hallmark of human ingenuity, and the greater a GenAI model's ability to use a given skill to solve problems, the greater its likelihood of replacing a human. For more than 7 in 10 skills assessed (70.7%), GenAI's ability to solve problems was rated a 3 ("moderate"). For more than a quarter (27.7%) of skills rated a 3 for problem-solving, the likelihood of GenAI replacing a human was also rated a 3. And for the 2.8% of skills at which GenAI rated its problem-solving ability a 4 ("good"), it also assigned itself a human replacement rating of 4 ("likely").

Let's remember, these tools do not yet have physical "bodies" that would allow them to perform manual tasks, though that may change in the future alongside advances in robotics. But until then, the necessity of manual, hands-on execution of a given skill plays an outsized role in helping determine its ultimate likelihood of GenAI replacement. The necessity of physical execution was rated as only "slightly necessary" or "not necessary" for all 101 skills that received a "good" rating (a 4 on the 5-point scale) for problem-solving. This shows that even if the GenAI model's ability to solve problems using a certain skill is relatively strong, as long as that skill requires hands-on execution GenAI will likely remain limited in its ability to replace a person. Of all skills analyzed, GenAI said it was "very unlikely" or "unlikely" to replace a human for almost a third (30.1%) of those skills where physical presence was determined to be "essential."

Impact on jobs

The distribution of skills at which GenAI is potentially better or worse is not equal across the thousands of occupations performed by millions of workers every day. Many tech roles, for example, require a greater proportion of digital skills that require limited hands-on execution and that are more likely to be able to be done at a high level by a GenAI model. Care roles, including nursing or child care, have a greater mix of skills for which GenAI may be able to provide some knowledge, but which are also more likely to require physical execution.

This analysis found that it is "possible" or "likely" that GenAI could replace a human at more than 71% of skills commonly found in job postings for software development occupations, for example. At the other end of the spectrum, only about 32.9% of skills found in the typical job posting for a nurse were rated as possible or likely to be replaced by GenAI.

Generally, the lower the share of skills in a given job that require physical execution, the higher the share of skills that could possibly or likely be replaced by GenAI.

A strong ability to solve problems also correlates with a higher likelihood of eventually replacing a human. But currently, GenAI isn't particularly strong at solving problems using skills found in many common jobs. Almost 78% of skills commonly found in a typical accounting occupation, for example, were rated as "possible" or "likely" to be replaced by GenAI, and GenAI said its problem-solving skills were "good" for 30.5% of skills common to accounting job postings. But accounting occupations are an outlier - it is the only occupation analyzed in which GenAI said it was at least "good" at problem solving for at least 30% of common skills. For most other occupations analyzed, the model's problem-solving abilities were more limited and the share of skills likely to be replaced by GenAI was also lower. If GenAI models improve their problem-solving abilities for more skills within more jobs, it's likely that the share of skills that may eventually be replaced in those jobs will also rise.

Common skills

Consider a nurse and a software developer. Both jobs typically require similar soft skills, including communication skills and leadership skills. But the skills specific to each job represent different kinds of skills at which GenAI has different abilities. GenAI is adept at learning and applying various coding languages like Python or JavaScript that are commonly found in software developer job postings, and in troubleshooting human-written code when needed. But while it may be able to help a nurse draw up an appropriate care plan for a patient, GenAI cannot actually administer physical care of any kind, including administering medication or conducting acute care.

On average, some 40.6% of software developer job postings require the skill "Java," a coding language. GenAI gives itself comparatively high ratings for theoretical knowledge (4 - "good"), problem-solving (3 - "moderate"), and replacement potential (3 - "possible") for Java. GenAI itself justifies its overall rating for the skill by saying, "The model can assist in solving coding problems, debugging, and generating code snippets, but complex problem-solving and integration require human expertise."

Within nursing, the opposite is true. One of the most common skills found in nursing job postings is (unsurprisingly) "nursing," found in 44.8% of nurse job postings, on average. For this specific skill, while GenAI gave itself a strong rating on theoretical knowledge (4 - "good"), it gave itself a comparatively low rating for solving problems (2 - "basic"). It deemed physical execution of the skill "essential," with the simple explanation that, "Nursing requires physical presence for patient care, administering medication, etc." Therefore, the replacement potential for the "nursing" skill was rated as "unlikely" (2) because, "The model can assist with information and advice but cannot replace the physical aspects of nursing."

In this example, nursing requires more skills at which GenAI is comparatively worse at problem-solving and which require some hands-on execution, so nursing jobs ultimately have a lower share of skills that are likely or possible to be replaced by GenAI. Physical presence was determined to be "highly necessary" or "essential" for a majority (58%) of skills commonly found in the typical nursing job posting. Physical presence was deemed highly necessary or essential for just 13% of skills commonly found in job postings for software developers.

Unrealized potential

Accounting is a good example of an occupation with a high share of skills that are neither especially likely or especially unlikely to be replaced by GenAI. A majority (58%) of skills commonly found in accounting job postings have only the possibility (a rating of 3 on the 5-point scale) of being replaced by GenAI, and the ultimate impact of GenAI on the accounting field will be determined by a host of as-yet-unknown factors.

A lot of accounting work doesn't require a physical presence - crunching numbers and determining profit/loss margins have long been strengths of digital software. But certain accounting businesses may require more hands-on work than others. For example, a small mom-and-pop accounting business may deal regularly with paper statements handed over by clients, or engage in regular face-to-face meetings with clients to discuss strategies, etc. In these cases, while GenAI may be adept at crunching the actual numbers, it cannot accept a batch of new documents or physically scan them once in possession of the business, nor can it laugh at a client's joke.

The ability to solve problems at a high level also greatly influences the overall likelihood of replacement for the skills common to accountant jobs. More than two-thirds of skills (68%) fall into the "moderate" problem-solving bucket for accountants (a rating of 3), largely because GenAI begins to falter as problems become more complex. For example, "organizational skills" (found in 18.8% of accounting job postings, on average) were assigned a "moderate" problem-solving ability because, "The model can offer practical solutions for organizing tasks and managing time, but may struggle with highly complex or dynamic scenarios." For the "accounting" skill (found in 74.8% of accounting job postings), it gave itself the same problem-solving rating because, "The model can solve moderate accounting problems and provide practical advice for common scenarios, but it may struggle with highly complex issues." It is possible that as the tools evolve and learn more, their ability to solve those more complex problems will also improve. But it's not definite. And so the ultimate likelihood of replacement for many of these skills remains only possible, and not (yet?) likely.

Looking ahead, heavy investments in digitization and electronic record-keeping - the kinds of investments that are likely to enable greater assistance from a GenAI helper - will be required to realize GenAI's full potential for a number of businesses and industries, including accounting. Every enterprise will need to decide for itself if these kinds of investments are worthwhile for them. Understanding the tools' strengths and weaknesses, as outlined in this analysis, can help business leaders determine which levers to pull and dials to turn to maximize their business and strike the right balance between human and digital help.

Conclusion

The biggest obstacle to widespread GenAI adoption may be the exacting nature of the tools themselves. Extracting as much useful information as possible from GenAI models can require a great deal of specificity and care when interacting with the tools. An answer generated in response to a loose prompt may change, by a little or a lot, when asked the same way a second time. More advanced queries, or asks for help with more advanced tasks, will require more-advanced prompt-writing skills. It is unrealistic to expect a meaningful share of workers to quickly learn the kinds of advanced prompt-engineering skills required to extract the most value from today's GenAI models. Companies and academic institutions will need to invest heavily in developing and deploying large-scale training programs that teach basic and advanced GenAI-specific skills. GenAI will find its way into daily workflows as more software and job-specific apps directly incorporate GenAI models (examples include Microsoft's Copilot, Salesforce's Einstein, or Github's Copilot products). Both approaches will take time.

The global workforce continues to age, and going forward the global labor market will need to keep growing productivity with fewer workers. GenAI tools that help maximize employee productivity are a necessary part of the solution to this challenge. They can potentially help fill skill gaps in certain industries and relieve some labor market tightness in areas where demand for certain types of jobs exceeds the ready supply of human workers. But this analysis shows that GenAI cannot be applied evenly across tight markets. If a market is short on skilled tech workers, GenAI is likely to be a bigger help than if a market is short on skilled healthcare workers. And the importance of physical necessity continues to loom large.

In jobs with a high share of skills that require hands-on execution, GenAI could help workers to refocus on the core skills necessary in these roles. In the healthcare field, it could be used to help document medical work, allowing a nurse or doctor to focus more on performing actual patient care or learning more about it. In more hands-off, stereotypical "office jobs," GenAI may potentially be able to obtain the same level of knowledge and be able to solve modest problems at a level similar to an early-career professional. This fact alone emphasizes the importance of continued human upskilling and career-long continuous learning in pursuit of expert knowledge, ensuring that the tools will reinforce a competent human, not replace them.

The takeaway is the same for all workers: While GenAI is highly unlikely to take a person's job, a person who knows how to use GenAI productively almost certainly will. Now is the time to explore these tools, learn how they function, evaluate their strengths and weaknesses, and assess how they could impact all types of work.
Methodology
This analysis utilized Indeed's skill database, which currently contains more than 2,800 skills common in US job postings, and a definition of those skills. Using generative artificial intelligence itself, we evaluated how GenAI could support those skills in a job setting.

The prompt developed for this evaluation was revisited over several months. We examined GenAI's abilities across three dimensions: Its ability to offer theoretical knowledge about the skill, its ability to apply that skill in problem-solving, and the significance of physical presence in effectively carrying out the skill. Each of these dimensions was rated on a 5-point scale, where a score of 1 represented minimal or no capability in that dimension, while a 5 signified high proficiency. These scores were then considered by the model in an ultimate determination of the overall likelihood that GenAI could replace human involvement across any of the 2,800+ individual skills, using a similar 5-point rating system.

We used the OpenAI API to send this data to GPT version gpt-4o-2024-05-13 and applied a temperature of 0. To increase the robustness of the results, we ran the full analysis 15 times. The final GenAI rating is a consolidation of the results of these 15 runs using the mode for each rating. In case of a tie, the lower value was taken, a conservative approach. All output was reviewed by the human authors and resulted in prompt revisions until all results were determined to be logical, robust, and repeatable.

We then joined these evaluations with job postings published on Indeed in the US between August 01, 2023, and July 31, 2024. We calculated the impact of GenAI across a cross-section of 16 occupations (and the skills mentioned in postings for these occupations) representing a wide range of the labor market, to calculate the impact of GenAI on certain common jobs.

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AI at Work: Why GenAI Is More Likely To Support Workers Than Replace Them - Indeed Hiring Lab

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Of the more than 2,800 unique work skills identified and assessed by Indeed, none are "very likely" to be replaced by generative artificial intelligence.

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Joy Buolamwini & The Algorithmic Justice League

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Joy Buolamwini & The Algorithmic Justice League

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Joy Buolamwini is a poet of code who uses art and research to illuminate the social implications of artificial intelligence. She founded the Algorithmic Justice League to create a world with more equitable and accountable technology. Her TED Featured Talk on algorithmic bias has over 1 million views. Her MIT thesis methodology uncovered large racial and gender bias in AI services from companies like Microsoft, IBM, and Amazon. Her research has been covered in over 40 countries, and as a renowned international speaker she has championed the need for algorithmic justice at the World Economic Forum and the United Nations. She serves on the Global Tech Panel convened by the vice president of European Commission to advise world leaders and technology executives on ways to reduce the harms of A.I.

As a creative science communicator, she has written op-eds on the impact of artificial intelligence for publications like TIME Magazine and New York Times. In her quest to tell stories that make daughters of diasporas dream and sons of privilege pause, her spoken word visual audit "AI, Ain't I A Woman?" which show... View full description

Joy Buolamwini is a poet of code who uses art and research to illuminate the social implications of artificial intelligence. She founded the Algorithmic Justice League to create a world with more equitable and accountable technology. Her TED Featured Talk on algorithmic bias has over 1 million views. Her MIT thesis methodology uncovered large racial and gender bias in AI services from companies like Microsoft, IBM, and Amazon. Her research has been covered in over 40 countries, and as a renowned international speaker she has championed the need for algorithmic justice at the World Economic Forum and the United Nations. She serves on the Global Tech Panel convened by the vice president of European Commission to advise world leaders and technology executives on ways to reduce the harms of A.I.

As a creative science communicator, she has written op-eds on the impact of artificial intelligence for publications like TIME Magazine and New York Times. In her quest to tell stories that make daughters of diasporas dream and sons of privilege pause, her spoken word visual audit "AI, Ain't I A Woman?" which shows AI failures on the faces of iconic women like Oprah Winfrey, Michelle Obama, and Serena Williams as well as the Coded Gaze short have been part of exhibitions ranging from the Museum of Fine Arts, Boston to the Barbican Centre, UK. A Rhodes Scholar and Fulbright Fellow, Joy has been named to notable lists including Bloomberg 50, Tech Review 35 under 35, BBC 100 Women, Forbes Top 50 Women in Tech (youngest), and Forbes 30 under 30. She holds two masters degrees from Oxford University and MIT; and a bachelor's degree in Computer Science from the Georgia Institute of Technology. A former pole vaulter, she still holds sentimental Olympic aspirations. Fortune Magazine named her to their 2019 list of world's greatest leaders describing her as "the conscience of the A.I. Revolution."

Learn more at www.poetofcode.com

Find her on twitter @jovialjoy and @ajlunited or connect on LinkedIn or Facebook.

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Person Overview ‹ Joy Buolamwini - MIT Media Lab

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WWW.POETOFCODE.COMJoy Buolamwini is a poet of code who uses art and research to illuminate the social implications of artificial intelligence. She founded the ...

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I am an Associate Professor of African American Studies at Princeton University, where I study the social dimensions of science, technology, and medicine. I am also the founder of the IDA B. WELLS Just Data Lab and the author of two books, People's Science (Stanford) and Race After Technology (Polity), and editor of Captivating Technology(Duke). I write, teach, and speak widely about the relationship between knowledge and power, race and citizenship, health and justice.

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I am an Associate Professor of African American Studies at Princeton University, where I study the social dimensions of science, technology, and medicine. I am also the founder of the IDA B. WELLS Just Data Lab and the author of two books, People's Science (Stanford) and Race After Technology (Polity), and editor of Captivating Technology (Duke).

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How AI discriminates and what that means for your Google habit: A conversation with internet studies scholar Safiya Noble

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The UCLA professor who put the study of algorithmic discrimination on the map turns her focus to our AI-powered future.

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Meredith Broussard Data journalist Meredith Broussard is an associate professor at the Arthur L. Carter Journalism Institute of New York University, research director at the NYU Alliance for Public Interest Technology, and the author of several books, including "More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech" and "Artificial Unintelligence: How Computers [...]

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Making Kin with the Machines: An Indigenous Perspective

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Making Kin with the Machines: An Indigenous Perspective

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Generative AI, the American worker, and the future of work | Brookings

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Addressing some of generative AI’s emerging risks for which society’s response is far less developed, especially risks to livelihoods.

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Data and Algorithms at Work: The Case for Worker Technology Rights

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A new report provides a comprehensive set of policy principles for worker technology rights in the United States.

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Work in Progress: A conversation with Debbie Compeau

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Work in Progress: A conversation with Debbie Compeau

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‘Generative AI is not going to take your job. People who know how to use generative AI are going to take your job.’

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‘Generative AI is not going to take your job. People who know how to use generative AI are going to take your job.’

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Work in Progress: Employers and higher education have a critical role in preparing for using generative AI in the workplace.

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Beyond Efficiency: A human-first AI adoption strategy

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Beyond Efficiency: A human-first AI adoption strategy

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Beyond efficiency: A human-first AI adoption strategy

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Candid Blog | Beth Kanter and Allison Fine weigh nonprofit AI tools’ risks and benefits, illustrating why a human-first AI adoption strategy is key to unlocking efficiency sustainably and responsibly.

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Includes resources on the contract workers powering AI in the US and globally.

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Our labor work pushes tech to employ high labor standards and support worker power rather than undermine it.

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Guiding Policy Principles for Responsible AI

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Guiding Policy Principles for Responsible AI

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AI Policy Principles - TechEquity Collaborative

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We need to ensure that AI and the practices of the tech industry are advancing human flourishing rather than undermining it.

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Get Started Welcome to the Artificial Intelligence (AI) & Inclusive Hiring Framework! This resource can help guide you through the process of making your AI-enabled hiring tools more inclusive and accessible for disabled job seekers. Below are a few important notes to help you get started. Pace yourself: Implement this resource in stages.

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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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Use Cases for Generative AI in Workforce Development

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New JFF research highlights current and potential uses for generative AI in workforce development and identifies existing tools and market opportunities.

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AI and the Future of Work | National Fund for Workforce Solutions

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There is no escaping the reality that AI and the future of work are inextricably linked. In our recent white paper “Unlocking Economic Prosperity”, we explored some of the ways AI and automation in…

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Think of AI as an umbrella term that covers a variety of leading-edge capabilities.

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This primer addresses the common questions that people have about artificial intelligence (AI) systems. It contains nontechnical explanations of common terms and concepts associated with AI.

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This primer addresses the common questions that people have about artificial intelligence (AI) systems. It contains nontechnical explanations of common terms and concepts associated with AI and gives examples of how to write about AI systems without misleading people about their capabilities. Although this primer was originally designed primarily for journalists, we have found that it is a valuable resource for anyone communicating about AI.

For a list of resources on where to find technical experts, please see Finding Experts in AI. For information specifically on generative AI, please see our accompanying guide Intro to Generative AI. Visit Emerging Tech Primers to see all primers.

What is artificial intelligence?

Artificial intelligence (AI) has historically referred to a collection of technologies designed to emulate human intelligence. In recent years, the term has become synonymous with machine learning, a set of computer processes used to identify unintuitive patterns in data. Examples of AI today include speech recognition, autonomous vehicle navigation, and the generation of new content, such as text or images.

Although the words "artificial intelligence" may conjure scenes from science fiction, most tools labeled with the term "AI" are not the powerful thinking machines of Hollywood movies. "Artificial General Intelligence," or AGI, is the widely-used term to refer to those not-yet-realized advanced technologies that could independently learn new capabilities. (See What is artificial general intelligence?) Historically, AI models were developed to accomplish specific individual tasks, but there are efforts to pursue AGI through combining some of these capabilities into "foundation models." Today's AI tools are more basic and already deeply embedded in a variety of sectors, including business, government, and even things as commonplace as the autofocus in your camera.

The definition of what counts as AI continues to evolve and remains a subject of debate. In fact, some take issue with the term "AI" altogether. (See Why don't people like the term "artificial intelligence?") Within this primer, "AI" will be used to refer to a collection of machine learning technologies that are designed to automate specific tasks.

Throughout the years, there have been several technologies that people have called "Artificial Intelligence"

What is machine learning?

Machine learning systems are a type of AI that are essentially pattern recognition tools. They are "trained" to identify patterns within large collections of data (such as text, images, and video) in order to produce a set of instructions, or a "model," which applies that "training" to new data. For instance, a machine learning model could be "trained" on news articles and then used to predict the next word in a sentence you are typing.

What is "the algorithm?"

Many people use the term "algorithm" colloquially to refer to a wide array of technologies (such as the Instagram algorithm, an encryption algorithm, or a facial recognition algorithm). Generally, the term "algorithm" is defined as a set of instructions for a computer to execute. However, when people talk about algorithms in relation to AI, they typically mean one of two related, but different, things:

What is data, and why is it so important?

Data, like AI, is an umbrella term that covers more than just numbers. The term describes many types of information that are stored and processed on computers. Videos, electronic health records, and the location information on your phone are all different kinds of data.

More reliable AI systems typically require a large amount of data to "train." This is because the patterns in a small collection of data may not be generalizable. For instance, a system built to label dogs in a collection of images may not operate reliably on images of dogs in a park if all of the examples used to "train" the system were of dogs in homes. Using more data from more diverse sources helps to ensure that the patterns represented in the machine learning model apply to a wide variety of contexts.

There is a tendency to misinterpret the accuracy of machine learning models as a measure of how well they represent reality. In fact, what academics and engineers often call the "accuracy" of AI systems is only a measure of how well they represent the data used to "train" them. If the data used to create a model is flawed (whether because it is incomplete or biased), the outputs of the model will reproduce or even amplify those flaws.

Non-exhaustive list of types of data

Text (books, articles, blog posts, discussion threads, social media, health records)

Images (photos, paintings and illustrations, x-rays, maps)

Audio (voice, music, birdsong, engine noise)

Video (CCTV, drone footage, film and television recordings)

Biometrics (face, fingerprint, heart rhythm, gestures)

Geolocation information

Having transparency into what data is used to "train" a model can give us insight into how the model responds to different examples. Generative AI systems like ChatGPT are trained on large swaths of the internet-knowing which parts of the internet are included in the training data makes ChatGPT's output more explainable.

Why don't people like the term "artificial intelligence?"

Many researchers and activists have argued that describing these systems as "intelligent" attributes too much agency to the technology itself and erases the humans involved in the process. When people talk about AI, they'll often say things like "an AI fired 3000 workers" or "DALL-E created award-winning art."

In reality, these tools-regardless of how much or little oversight they receive-do not exist in a vacuum. Humans choose what types of systems to develop and curate the data to "train" machine learning models. Humans define the criteria for good system performance, and humans deploy the resulting technology-even if they abdicate responsibility for its impacts. Critics argue that calling these tools "artificial intelligence" obfuscates the human roles in these processes and makes it difficult for people impacted by the deployment of AI to seek remedy or recourse. Although there are compelling arguments for abandoning terms like "artificial intelligence" and "machine learning," they are nonetheless already in wide use. Rather than avoiding these terms, it may be more pragmatic to help the public contextualize them by both explaining what specific technologies constitute the AI systems being discussed and to highlight the people involved in building and using these tools. (See How to Talk About AI for examples.)

What is artificial general intelligence?

Artificial general intelligence (AGI), sometimes referred to as "strong AI," is a conceptual computational tool that exhibits human-level or beyond human-level intelligence in all domains. Some people believe it is important to pursue AGI because a machine that has generalizable, human-level intelligence could be a useful tool for space exploration, national security, or neuroscience. No such capabilities currently exist, though many companies are actively pursuing this possibility.

Throughout history, different tests have been proposed for identifying intelligence in AI systems, including playing chess, making a cup of coffee, or the "Turing test," which, to pass, a human must fail to identify the AI in conversation. All of these have since been ruled insufficient indicators of human-like intelligence. Until the more philosophical question of how to define intelligence is addressed, there may be no agreed upon way to evaluate attempts to make these kinds of systems.

How to talk about AI

This section showcases examples of how to write about AI inspired by news stories from the last year. Reporting on AI should avoid personifying the technology or obfuscating the people and organizations using the tools. Instead, aim for sentences that clearly highlight both the types of technology being used and the people involved in their design and deployment. Researchers are often useful sources for getting more information on the specific capabilities and limitations of AI systems. (See Common Roles in AI for more information.)

In general, accurate descriptions include the following:

People who use specific AI tools or capabilities to do tasks.

examples

Employers are automating data-entry and operational tasks by deploying natural language processing AI, which could put some administrative jobs at risk.

People are using DALL-E to create art, which has professional illustrators worried about their futures.

What has become apparent is that, even though large language models can be used to generate reasonable language, the models themselves lack the capacity to "know" what they are doing, and even experts are unsure of how the models perform as well as they do.

The Post reported that local governments are deploying facial-recognition cameras to "scan everyone who walks past them," looking for people who are banned from public housing.

While these tools are not designed to be used for medical applications, patients are asking text-generators like ChatGPT to self-diagnose their medical conditions.

Communicate clearly about AI using only a few simple ingredients

AI 101 © 2023 by Aspen Digital is licensed under CC BY-NC 4.0.

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A resource addressing the common questions that many people have about artificial intelligence (AI) systems.

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AI and the Black Community: Navigating the Future of Work

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Black workers face unique challenges in the AI era. Ensuring equity is crucial for a just, prosperous future.

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A call to action to address inequity in AI access 

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Inside Higher Ed | Higher Education News, Events and Jobs

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Leaders in higher education must take decisive actions to prevent artificial intelligence (AI) from further exacerbating inequality.

AI literacy has already become a gating qualification for participants across America's workforce. In one recent survey by Amazon Web Services, a staggering 73 percent of employers report prioritizing hiring talent with AI skills. Those employers are willing to pay candidates with AI expertise significantly higher salaries-in some cases almost 50 percent more. Equipping students for career success and social mobility therefore requires an immediate, holistic and collective approach to building AI literacy. To do so, we must first begin with access while carefully examining both policy and pedagogy.

There is a notable disparity among students' perceptions of and openness to AI technology and tools. A fall 2023 survey by San Diego State University captured impressions from nearly 8,000 current college students, revealing that more than 71 percent agree that AI will become an essential part of most professions. However, survey results also indicated that students who had more access to technology- those who own more smart devices-were more likely to view AI positively and less likely to find AI tools intimidating or complex. Reinforcing the point, when data from the SDSU survey was disaggregated to compare university students living in a populous urban area compared with those living in California's most economically challenged county, the students from the latter community report far lower use of AI tools, far lower comfort with or understanding of those tools, and far less positive impact from those tools on their educational experience.

First-generation, low-income and underrepresented minority students are already behind and enter higher education with a digital divide already in place. This was further illustrated by recent research from the Public Policy Institute of California on disparate access to digital devices and the internet for K-12 students in the nation's largest state public-school system. Put simply, students who are already at an educational and digital disadvantage because of family income and first-generation constraints are becoming even more so every day as their peers embrace AI at high rates as a productivity tool-and they do not.

Further compounding the threat of divergent access and perceptions of AI technology is a growing trend toward commodification of AI tools. While some tools will remain free, it appears that the most powerful and modern tools will increasingly come at a cost. The U.K.-based nonprofit Jisc recently reported that access to a full suite of the most popular generative AI tools and education plug-ins currently available could cost about £1,000 (about $1,275) per year. For many students already accumulating student debt and managing the rising cost of living, paying more than $100 per month for competitive AI tools is simply not viable.

And finally, despite the increasing integration of AI into coursework, SDSU's recent survey was one of many to highlight that only a fraction of students report encouragement from professors or consistency in policies for use of AI resources.

In short, as the need for and value of AI skills continues to increase, there are vulnerable segments of our communities that either feel negatively about it, cannot afford it or do not have clear direction for its acceptable and ethical use, and are therefore fearful their use of AI could cost them their education or career. There is a critical need to address access, understanding and perception issues among our most high-need populations to prevent AI from widening the already pervasive digital divide.

Universities-and especially public universities that were created to serve their communities-are in a powerful position to fulfill this social responsibility. They are uniquely positioned and capable of ensuring that as AI drives innovation and progress, it does not simultaneously drive disparity. To this end, there are four key actions that higher education institutions can and should take right away to level the playing field, protect equity and drive opportunity.

First, colleges and universities must forge partnerships for AI affordability and access. An individual institution holds little power or leverage in conducting business with the tech giants leading the AI charge. However, forging partnerships and collaborations among higher education institutions will enable the collective negotiation of fair pricing models with AI vendors that prioritize accessibility for students. Discounts offered to educational institutions should be substantial, reflecting the imperative of affordability for all. To this end, SDSU has launched the Equitable AI Alliance (EAIA) and is actively discussing this important topic with universities across the state and country.

Second, higher education leaders must embrace data-driven discussions and decision making to inform policies and practices surrounding AI in education. As AI evolves, adding ever more complex capabilities around generative text, video, sound, creative media and more, so too will student expectations and usage. Further, universities should gather and pool both current and longitudinal data on changing perceptions and use to create deep and representative understandings of the national availability and use of AI and its impact on student learning and employability.

Third, acknowledging the discrepancy between student and faculty perceptions and inconsistent policies for the use of AI tools in academics, universities must provide training on the acceptable and responsible use of AI technology, including on recognizing bias and the need to keep a human in the loop. Free, basic training for students, faculty and staff will ensure a smoother navigation of the new digital landscape. Certification programs for each of these audiences can be introduced and encouraged as a pathway for those seeking more in-depth expertise.

And finally, institutional leaders must prioritize the development of comprehensive AI strategies. Input for these strategies must come from heterogeneous stakeholders at all levels of the organization-including students. Along with setting roadmaps and milestones for adoption of AI technology in the classroom, these teams must prioritize equity, affordability and responsible usage as core pillars of their work. If your university is not already developing responsible use guidelines for AI, then it is choosing to turn a blind eye instead.

Now is the time to act. As AI continues to reshape the educational landscape, we must prioritize affordability, equity and responsible usage so that all students have the opportunity to thrive in an increasingly AI-driven world. We extend an invitation and challenge to our fellow higher education leaders to join in this critical endeavor. Let us seize this moment in embracing the vast potential of AI while paving the way for a more inclusive future for all students.

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A call to action to address inequity in AI access (opinion)

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Leaders must take steps to prevent low-income and first-gen students from falling further behind, Adela de la Torre and James Frazee write. Leaders in higher education must take decisive actions to prevent artificial intelligence (AI) from further exacerbating inequality.

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Science & Tech

Eileen O'Grady

7 min read

Tech offers promise to help yet too often perpetuates ableism, say researchers. It doesn't have to be this way.
Third in a four-part series on non-apparent disabilities.
AI researcher Naomi Saphra faced "a programmer's worst nightmare" in 2015. After a decade of coding and just as she was about to start a Ph.D. program in Scotland, neuropathy in her hands rendered typing too painful.

Seeking a solution, Saphra turned to the very technology she was studying. She began the long process of teaching herself how to code using voice-to-text dictation technologies. Today, a system called Talon, which Saphra has heavily customized to complete specific tasks, allows her to code and to write papers for her research on language models.

"I rely on it completely," said the research fellow at the Kempner Institute for the Study of Natural and Artificial Intelligence. "I would absolutely not have a career if we didn't have AI for speech-to-text. These days it's pretty hard to exist in the world if you're not able to use a computer much. And as things have advanced, it's been very important that the word error rate has gotten lower over the years."

AI technology can be a powerful assistive tool for people like Saphra with non-apparent disabilities - physical or mental conditions that are not immediately obvious to others. But disability advocates say these tools have a long way to go to become truly accessible. Experts say including disabled people in conversations on AI fairness and the development process is key.

Lawrence Weru, an associate in biomedical informatics at Harvard Medical School, was initially excited when voice-activated AI tools such as Siri and Alexa were released in the early 2010s. As someone who learned to code from a young age on public library computers before personal computers were common, he has long been fascinated by advances in digital technology. But Weru, who has a stutter, quickly found voice-activated technology more frustrating than helpful. When asking Siri for directions, the digital assistant would not understand the question if he stuttered.

While this was hardly a new experience - before AI, Weru remembers the frustration of trying to contact his bank and not being able to get past the automated phone system - it was disappointing to realize that the AI likely had not been trained on data from people with disabilities like his.

"People create things and people always have a vision in mind of who is going to be using their thing," Weru said. "Sometimes not everybody is included in those personas."

His experience with Siri makes Weru concerned about the future of voice-activated AI technology, envisioning a world in which critical tasks - making doctor appointments, applying for jobs, accessing education - are powered not by humans, but by technologies that can't be used by everyone.

"If we're creating tools that we know are fed with information that can bias against certain groups, and we integrate those into very crucial aspects of our lives, what's going to be the impact of that?" Weru said. "That's a concern that I hope people would be having enough foresight to try to address in advance, but historically accessibility is usually something that's treated as an afterthought."

Maitreya Shah, a fellow at the Berkman Klein Center for Internet and Society, recently launched a research project analyzing different schools of thought on "AI fairness," or movements seeking to mitigate AI bias against people in marginalized groups. Shah, a blind lawyer and researcher, wants to go beyond conversations about accessibility and examine what he believes is the root of the issue: People with disabilities are not being included in conversations about AI, even in conversations about AI fairness.

"A lot of research so far has focused on how AI technologies discriminate against people with disabilities, how algorithms harm people with disabilities," Shah said. "My aim for this project is to talk about how even the conversation on AI fairness, which was purportedly commenced to fix AI systems and to mitigate harms, also does not adequately account for the rights, challenges, and lived experiences of people with disabilities."

For his research, he's interviewing scholars who have studied the issue and evaluating frameworks designed to maintain AI fairness proposed by governments and the AI industry.

Shah said developers often consider disability data to be "outlier data," or data that differs greatly from the overall pattern and is sometimes excluded. But even when it's included, there are some disabilities - like non-apparent disabilities - that are overlooked more than others. If an AI is trained on a narrow "definition" of disability (like if data from people who stutter is not used to train a voice-activated AI tool) the outcome will be that the tool is not accessible.

"There is a paradox," Shah said. "If you don't incorporate disability data, your algorithms would be open to discriminating against people with disabilities because they don't fit the normative ideas of your algorithms. If you incorporate the data, a lot of people with disabilities would still be missed out because inherently, the way you incorporate datasets, you divide data on the axes of identity."

"Do people with autism or other disabilities even want these technologies? No one asks them."
Maitreya Shah

In his own life, Shah uses some AI technologies as assistive tools including "Be My AI," which describes images, and "Seeing AI," which provides users with visual information such as text, color, light, and scenery. Blind people were very involved in the development and testing process for both those tools.

But Shah said too often people with disabilities are not included in the high-level decision-making and development processes for AI that is purported to benefit them. He cited, as an example, technology designed to diagnose autism or address learning disabilities.

"The question is: Do people with autism or other disabilities even want these technologies? No one asks them," Shah said.

In his research, Shah proposes adopting perspectives from disability justice principles, such as participation.

"Let people with disabilities participate in the development and the deployment of technologies. Let them decide what is good for them, let them decide how they want to define or shape their own identities."

Saphra agrees, which is why she believes any developer creating assistive AI should make it easily customizable, not just by AI experts or coders, but by people who may not be tech experts. That way, users can set up the system to perform specific, essential tasks like Saphra did for writing code.

"It's very important to make sure that everything you release is hackable, everything is open-source, and that everything has an accessible interface to start with," Saphra said. "These things are going to make it more useful for the greatest number of disabled people."

4 students with conditions ranging from diabetes to narcolepsy describe daily challenges that may not be obvious to their classmates and professors

How to ensure students with disabilities have an equal chance to succeed?

Harvard lab's research suggests at-risk kids can be identified before they ever struggle in school

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Why AI fairness conversations must include disabled people - Harvard Gazette

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Technology offers promise to help yet too often perpetuates ableism, say researchers. It doesn't have to be this way.

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In late 2022, Jennifer Abruzzo, general counsel of the National Labor Relations Board (NLRB), wrote a memo warning that artificial intelligence-enabled monitoring of labor organizing activities might violate the rights granted to workers by Section 7 of the National Labor Relations Act (NLRA).[1] While electronic surveillance pervades American workplaces and artificial intelligence increasingly performs tasks formerly given to people throughout all sectors (both serious issues in their own right), AI is particularly threatening in the labor organizing context given employers' propensity for retaliation and intimidation in dismantling workplace unionization campaigns.[2] However, given the NLRA's protections of union activity are uniquely potent among rights in the workplace, AI surveillance of labor organizing may be particularly vulnerable to regulation. The NLRA guarantees that workers be free from hindrances to labor organizing; any form of threat or retaliatory adverse action by an employer qualifies as a prohibited unfair labor practice under Section 8.[3] Despite the NLRB general counsel's warning that AI surveillance undermining Section 7 rights is necessarily unlawful, there is evidence that employers have already been using AI to undermine unionization in the workplace.[4] As AI continues to grow in importance and availability, enforcement of the NLRA against such employers, as urged by the general counsel of the NLRB, will be crucial for the future of the labor movement.[5]

The fall of 2023 marked several public victories for the labor movement: The SAG, WGA, and UAW strikes and subsequent contract renegotiations put unions in the headlines more than any time in recent memory.[6] In an era of growing corporate wealth and power, workers have seized labor organizing as a tool of resistance to the pressures their employers have placed upon them.[7] At the same time, however, employers have pushed back, using surveillance tools (including AI technology initially designed for military use) to undermine workplace unionization drives.[8] Employers have adopted AI to determine employees' propensity to organize, to monitor communications among employees, and to administer quotas to systematically disadvantage union supporters (even where the quotas are facially neutral).[9]

This sort of anti-union use of AI monitoring is an open secret at workplaces like Amazon, which are already defined by both traditional and AI-enabled surveillance.[10] In workplaces where every step, every conversation, and every bathroom break is recorded and retained, what's to stop employers from merely adjusting how they use that information to detect union organizing? Indeed, monitoring workers is no longer the realm of especially controlling employers but is now the norm: It seems a majority of employers currently employ some sort of tracking technology, a shift which appears to have been inspired by the changing workplace norms brought on by the pandemic.[11] Whether or not safety was their true motive, employers took the opportunity presented by the pandemic to introduce new and more invasive forms of surveillance: Amazon, for example, introduced a system of AI-enabled cameras to track employees' whereabouts, purportedly to ensure social distancing in its plants.[12] Amazon, which uses extensive camera tracking (both AI and human-supervised) to monitor its warehouse employees, also counts time spent interacting with other workers as "time off task," a metric that, if logged for more than 30 minutes in a single day, can result in a written warning.[13] Even when surveillance is not explicitly directed at union organizing activity, it targets the same activities necessary to union organizing: Interaction with other employees (often at the job site) is necessary to collecting the signatures needed to begin seeking union representation.[14] Additionally, remote workers' interactions with other employees may be equally subject to AI scrutiny for organizing tendencies: Interactions between some workers have been tracked using AI initially designed to map terrorist cells, put to new use to detect groups of workers interested in seeking union representation.[15]

Under the NLRA, employer practices that burden Section 7 rights must be weighed against employers' legitimate interests; the latter must outweigh the former in order to continue.[16] Troublingly, however, those selling AI surveillance systems designed for monitoring factories tout them as tools for ensuring compliance with extant employment law.[17] Vantiq and SmartCow, two companies selling ready-to-use AI cameras for factory monitoring, claim in their advertising materials that AI monitoring will detect injured workers but fail to mention the rest of the (NLRA-protected) movements the systems will inevitably flag.[18] Even the European Agency for Safety and Health at Work (EU-OSHA) claims that AI monitoring of the workplace will help to improve worker safety through PPE compliance, among other mechanisms.[19] The safety justification is problematic if used as a pretext for more pervasive monitoring that impinges upon employees' rights to organize. The NLRB weighs legitimate justifications employers might have against the burden on labor organizing activities, meaning that even if employers assert the necessity of such systems, their impact on workers' rights under the NLRA might limit how AI can be used in the workplace.[20]

The Department of Labor (DOL) has a new opportunity to make exceptionally clear through rulemaking that employers' burdening of protected labor activity through AI surveillance violates the NLRA. The Biden administration issued an executive order on the future of artificial intelligence on October 30, 2023, Section 6 of which gave DOL 180 days to promulgate a rule clarifying the protected-activity implications of AI surveillance and reaffirming existing law in the face of AI.[21] DOL and the NLRB now both have the chance to push back against the trend of workplace AI surveillance and its detrimental effect on labor organizing: With the future of work itself so uncertain given the rising dominance of AI, labor will be more critical than ever.

[1] Electronic Monitoring and Algorithmic Management of Employees Interfering with the Exercise of Section 7 Rights, NLRB Memorandum GC 23-02, at 1 (Oct. 31, 2022), available at https://s3.documentcloud.org/documents/23253403/nlrb-abruzzo-electronic-monitoring-algorithms-section-7-rights.pdf.

[4] Gabriel Grill & Christian Sandvig, Military AI's Next Frontier: Your Work Computer, Wired (Jun. 22, 2023), https://www.wired.com/story/military-ais-next-frontier-your-work-computer/.

Paul Prescod, US Labor Is Having a Movement Moment, Jacobin (Nov. 21, 2023), https://jacobin.com/2023/11/us-labor-movement-uaw-strikes-sag-aftra-wga-union-reform.

Lauren Kaori Gurley, Internal Documents Show Amazon's Dystopian System for Tracking Workers Every Minute of Their Shifts, Vice (Jun. 2, 2022), https://www.vice.com/en/article/5dgn73/internal-documents-show-amazons-dystopian-system-for-tracking-workers-every-minute-of-their-shifts.

Wendi S. Lazar & Cody Yorke, Watched While working: Use of Monitoring and AI in the Workplace Increases, Reuters (Apr. 25, 2023), https://www.reuters.com/legal/legalindustry/watched-while-working-use-monitoring-ai-workplace-increases-2023-04-25/.

Tom Simonite, Amazon Touts AI for Social Distancing Amid Worker Complaints, Wired (Jun. 18. 2020), https://www.wired.com/story/amazon-touts-ai-social-distancing-worker-complaints/.

Tomas Chamorro-Premuzic, Can Surveillance AI Make the Workplace Safe?, MIT Sloan Mgmt. Rev. (Aug. 4, 2020), https://sloanreview.mit.edu/article/can-surveillance-ai-make-the-workplace-safe/#article-authors.

[18] See Vantiq's claims about its factory safety AI technology at https://vantiq.com/connect/solution/ai-watching-system-for-factory-workers/ and SmartCow's at https://www.smartcow.ai/use-case-ai-cameras-for-smarter-factories.

Nick Warburton, EU-OSHA Publishes Three Policy Briefs on Smart Digital Systems for OSH, IOSH Magazine (Jun. 5, 2023), https://www.ioshmagazine.com/2023/06/05/eu-osha-publishes-three-policy-briefs-smart-digital-systems-osh.

[21] Exec. Order No. 14110, 88 Fed. Reg. 75191, at 75210 (Oct. 30, 2023).

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Labor Organizing and AI Surveillance in the Workplace

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In late 2022, Jennifer Abruzzo, general counsel of the National Labor Relations Board (NLRB), wrote a memo warning that artificial intelligence-enabled monitoring of labor organizing activities might violate the rights granted to workers by Section 7 of the National Labor Relations Act (NLRA).[1] While electronic surveillance pervades American workplaces and

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The impact of generative AI on Black communities

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We explore the ongoing impact of generative AI on the racial economic gap in the US, and how it could be used to remove barriers to Black economic mobility.

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Magazine Fall 2020 Issue Frontiers

It's possible to protect employees' health and well-being by digitally monitoring their behavior, but how far should companies go?

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As the world recovers from the initial shock wave caused by the COVID-19 pandemic, businesses are preparing for their transitions back to their physical workplaces. In most cases, they are opening up gradually, with an unprecedented focus on keeping workers safe as they return. To protect employees' health and well-being, organizations must systematically reengineer their workspaces. This may include reconfiguring offices, rearranging desks, changing people's shifts to minimize crowding, and allowing people to work remotely long term. Then there are the purely medical measures, such as regular temperature checks, the provision of face masks and other personal protective equipment, and even onsite doctors. Such precautions would have seemed extreme a short time ago but are quickly becoming accepted practices.

Soon, organizations may also start monitoring employees' whereabouts and behavior more closely than ever, using surveillance tools such as cellphone apps, office sensors, and algorithms that scrape and analyze the vast amounts of data people produce as they work. Even when they are not in the office, workers generate a sea of data through their interactions on email, Slack, text and instant messaging platforms, videoconferences, and the still-not-extinct phone call. With the help of AI, this data can be translated into automated, real-time diagnostics that gauge people's health and well-being, their current risk levels, and their likelihood of future risk. A parallel here is the use of various surveillance measures by many governments - with some success - to contain the pandemic (in China, Israel, Singapore, and Australia, for instance). Most notably, track-and-trace tools have been deployed to follow people's every move, with the intention of isolating infected individuals and reducing contagion. 1

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Technological surveillance is always controversial, not least for being out of sync with the basic values and tenets underpinning free and democratic societies. Indeed, the idea of using AI to do previously "human" tasks is already quite polarizing; for many of us, the notion of also using it for surveillance adds an element of Orwellian creepiness.

And yet, even in free and democratic societies, we have already given away so much personal data in the digital age that the concept of privacy has been greatly devalued and diluted.

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About the Author

Tomas Chamorro-Premuzic (@drtcp) is the chief talent scientist at ManpowerGroup, a professor of business psychology at University College London and Columbia University, and an associate at Harvard's Entrepreneurial Finance Lab. He is the author of Why Do So Many Incompetent Men Become Leaders? (and How to Fix It) (Harvard Business Review Press, 2019).

References

1. S. Bond, "Apple and Google Build Smartphone Tool to Track COVID-19," NPR, April 10, 2020, www.npr.org.

2. J. Bersin and T. Chamorro-Premuzic, "New Ways to Gauge Talent and Potential," MIT Sloan Management Review 60, no. 2 (winter 2019): 7-10; and H. Schellmann, "How Job Interviews Will Transform in the Next Decade," The Wall Street Journal, Jan. 7, 2020, www.wsj.com.

3. R.A. Calvo, D.N. Milne, M.S. Hussain, et al., "Natural Language Processing in Mental Health Applications Using Non-Clinical Texts," Natural Language Engineering 23, no. 5 (September 2017): 649-685.

4. P. Wlodarczak, J. Soar, and M. Ally, "Multimedia Data Mining Using Deep Learning," in "Fifth International Conference on Digital Information Processing and Communications" (Sierre, Switzerland: IEEE, 2015).

5. B. Dattner, T. Chamorro-Premuzic, R. Buchband, et al., "The Legal and Ethical Implications of Using AI in Hiring," Harvard Business Review, April 25, 2019, https://hbr.org.

6. A. Agrawal, J. Gans, and A. Goldfarb, "Prediction Machines: The Simple Economics of Artificial Intelligence" (Boston: Harvard Business Review Press, 2018).

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Jobs of Tomorrow: Large Language Models and Jobs

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Jobs of Tomorrow: Large Language Models and Jobs

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GenAI Will Bring Unprecedented Workforce Disruption

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Generative AI (GenAI) will transform roles and lead to widespread displacement of workers. How should business leaders respond?

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Artificial Intelligence & The Future Of Work Survey Highlights

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Artificial Intelligence & The Future Of Work Survey Highlights

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Pew Research Center's Social & Demographic Trends Project

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About a fifth of all workers have high-exposure jobs; women, Asian, college-educated and higher-paid workers are more exposed. But those in the most exposed industries are more likely to say AI will help more than hurt them personally

Pew Research Center conducted this study to understand how American workers may be exposed to artificial intelligence (AI) at their jobs. The study emphasizes the impact of AI on different groups of workers, such as men and women and racial and ethnic groups, and it includes new survey findings on how American adults think AI will impact them personally, setting it apart from preceding analyses.

By exposure to AI, we refer to the likelihood that the activities workers perform on their jobs may be replaced or aided by artificial intelligence. We make no determination as to whether workers may lose their jobs as a result or gain new jobs, and we also do not consider the role of robots.

Most of the analysis is based on data from the Occupational Information Network (O*NET), Version 27.3. O*NET analysts rate the importance of 41 work activities related to job performance in individual occupations. We grouped these activities into three categories based on their likely exposure to AI: low exposure, medium exposure and high exposure. Occupations were ranked by the relative importance of low- or high-exposure activities. Those in the top quarter of each ranking are the least or most exposed occupations. The remaining occupations have medium exposure.

Data on the employment and earnings of workers in individual occupations and their demographic profiles are from the Current Population Survey ( IPUMS). Monthly files from January to December 2022 were combined to form an annual file. Earnings data is available for a quarter of this sample. The AI-exposure rankings of 873 detailed occupations from the O*NET data are matched to 485 broader occupations listed in the CPS.

A part of the analysis is based on a Pew Research Center survey of 11,004 U.S. adults conducted from Dec. 12 to 18, 2022. Everyone who took part in the survey is a member of the Center's American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP's methodology.

Here are the questions used for this report, along with responses, and its methodology.

Artificial intelligence (AI): Broadly speaking, AI refers to a range of applications of machine learning, computer vision and natural language processing that allow computers or machines to perform tasks autonomously. AI can substitute for or complement a variety of human tasks, such as writing, drawing, providing customer service, reading radiology scans, driving cars and more. ChatGPT and Dall-E are examples of AI-driven technologies.

Low or high exposure to AI: We rated a set of 41 job-related work activities common to all occupations as having low, medium or high exposure to AI. How much an occupation is exposed to AI depends on which of these work activities is more important in that particular job. The importance scale runs from one (not important) to five (extremely important). In this report, we focus on the importance of low- and high-exposure activities. In one step of analysis, occupations are ranked by the relative importance of high-exposure activities in them; the jobs ranking in the top 25% are occupations that we rate as most exposed to AI. In another step, occupations are ranked by the relative importance of low-exposure activities in them. Those ranking in the top 25% are occupations that are least exposed to AI. A full description of the ranking process is in the methodology.

The terms occupation and job are often used interchangeably in the report, as are the terms earnings and wages.

White, Black, Asian and American Indian or Pacific Islander workers include those who report being only one race and are not Hispanic. Hispanics are of any race.

High school graduate refers to those who have a high school diploma or its equivalent, such as a General Education Development (GED) certificate, and those who had completed 12th grade, but their diploma status was unclear (those who had finished 12th grade but not received a diploma are excluded). Adults with some college include those with an associate degree and those who attended college but did not obtain a degree.

U.S. born refers to individuals who are U.S. citizens at birth, including people born in the 50 U.S. states, the District of Columbia, Puerto Rico or other U.S. territories, as well as those born elsewhere to at least one parent who is a U.S. citizen. The terms foreign born and immigrant are used interchangeably in this report. They refer to people who are not U.S. citizens at birth.

Artificial intelligence (AI) recently gained new attention with the release of ChatGPT and Dall-E. These tools and the broader array of AI-driven business applications represent a new reality for workers.

Historically, changes in technology have often automated physical tasks, such as those performed on factory floors. But AI performs more like human brainpower and, as its reach grows, that has raised questions about its impact on professional and other office jobs - questions that Pew Research Center seeks to address in a new analysis of government data.

What we found

In 2022, 19% of American workers were in jobs that are the most exposed to AI, in which the most important activities may be either replaced or assisted by AI.

23% of workers have jobs that are the least exposed to AI, in which the most important activities are farther from the reach of AI. Other workers, nearly six-in-ten in all, are likely to have varying levels of exposure to AI.

Jobs with a high level of exposure to AI tend to be in higher-paying fields where a college education and analytical skills can be a plus.

Certain groups of workers have higher levels of exposure to AI

Those with more education: Workers with a bachelor's degree or more (27%) are more than twice as likely as those with a high school diploma only (12%) to see the most exposure.

Women: A greater share of women (21%) than men (17%) are likely to see the most exposure to AI. This is because of differences in the types of jobs held by men and women.

Asian and White: Asian (24%) and White (20%) workers are more exposed than Black (15%) and Hispanic (13%) workers.

Higher-wage workers: In 2022, workers in the most exposed jobs earned $33 per hour, on average, compared with $20 in jobs with the least amount of exposure.

Workers seem more hopeful than concerned about the impact of AI on their jobs

A recent Pew Research Center survey finds that many U.S. workers in more exposed industries do not feel their jobs are at risk - they are more likely to say AI will help more than hurt them personally. For instance, 32% of workers in information and technology say AI will help more than hurt them personally, compared with 11% who say it will hurt more than it helps.

Which jobs are more exposed to AI? Work-related tasks vary in their exposure to AI. Some activities, such as repairing equipment, may have low exposure to AI, while others may have a medium or a high degree of exposure. Also, activities with different levels of exposure may be equally important within many jobs.

In our analysis, jobs are considered more exposed to artificial intelligence if AI can either perform their most important activities entirely or help with them.

For example, AI could replace, at least to a degree, the tasks "getting information" and "analyzing data or information," or it could help with "working with computers." These are also among the key tasks for judicial law clerks and web developers, and they are more exposed to AI than other workers. However, AI alone cannot "assist and care for others" or "perform general physical activities." Thus, nannies - for whom these are essential activities - are less exposed to AI.

In our analysis, jobs that placed in the top 25% when ranked by the importance of work activities with high exposure to AI were judged to be the most exposed. Jobs that placed in the top 25% when ranked by the importance of work activities with low exposure to AI are the least exposed. The remaining jobs, such as chief executives, are likely to see a medium level of exposure to AI. (Refer to the appendix for an extended list of examples of occupations in each group.)
Related: How we determined the degree to which jobs are exposed to artificial intelligence
Will exposure to AI lead to job losses? The answer to this is unclear. Because AI could be used either to replace or complement what workers do, it is not known exactly which or how many jobs are in peril. For this reason, our study focuses on the level of exposure jobs have to AI. It sets aside the question of whether this exposure will lead to jobs lost or jobs gained.

Consider customer service agents. Evidence shows that AI could either replace them with more powerful chatbots or it could enhance their productivity. AI may also create new types of jobs for more skilled workers - much as the internet age generated new classes of jobs such as web developers. Another way AI-related developments might increase employment levels is by giving a boost to the economy by elevating productivity and creating more jobs overall.

Overall, AI is designed to mimic cognitive functions, and it is likely that higher-paying, white-collar jobs will see a fair amount of exposure to the technology. But our analysis doesn't consider the role of AI-enabled machines or robots that may perform mechanical or physical tasks. Recent evidence suggests that industrial robots may reduce both employment and wages. Moreover, jobs held by low-wage workers, those without a high school diploma, and younger men are more exposed to the effects of industrial robots.

What data did we use? This analysis rests on data on the importance of 41 essential work activities in 873 occupations from the U.S. Department of Labor's Occupational Information Network (O*NET, Version 27.3). We used our judgment to determine which of these activities have low, medium or high exposure to AI, but focus on the importance of low- and high-exposure activities. For additional analysis, the 873 occupations were further grouped to a total of 485 for which government data on employment and earnings of workers were available. That allowed us to analyze the potential impact of AI on different groups of workers. Other findings about how workers feel about AI come from a Center survey of 11,004 U.S. adults conducted between Dec. 12 and 18, 2022. (Refer to the text boxes and methodology for more details.)

Our other key findings

Most workers are more likely to work in jobs with less exposure to AI than in jobs with more exposure. This is true among men, Black and Hispanic workers, younger workers, and workers with less formal education, among others.

Asian workers and college graduates are among the highest paid of workers most exposed to AI. The most exposed workers earn more than the least exposed workers no matter their demographic characteristic, and the gap is especially striking among men, Asian workers and foreign-born workers.

Analytical skills are more important in jobs with more exposure to AI. These skills include critical thinking, writing, science and mathematics. Mechanical skills, such as equipment maintenance, are more important in jobs with less exposure to AI.

Scarcely any U.S. businesses - fewer than 3% - reported using advanced technologies such as machine learning or machine vision software to produce goods or services in 2020, according to the most recent available data from the U.S. Census Bureau. Still, these were large businesses who accounted for about 11% to 16% of overall employment.

Sidebar: How we determined the degree to which jobs are exposed to artificial intelligence

In our analysis, we considered two major questions when assessing the exposure of jobs to AI:

1. What is the likelihood that a work activity may be substituted for or complemented by AI at this time? Is the likelihood high, medium or low?

2. How important are activities with high or low exposure to AI in any given job, relative to the importance of other activities?

Classifying work activities by exposure to AI

The O*NET database lists a set of 41 work activities in common across all occupations. Examples of these activities are getting information, selling or influencing others, and handling and moving objects (refer to the methodology for the complete list). We used our collective judgment to designate each activity as having high, medium or low exposure to AI. Consensus on some activities, such as performing general physical activities or processing information, was reached quickly. The former is judged as having low exposure to AI and the latter is judged as having high exposure.

In other instances, we used additional details on a work activity to reach consensus. The question we asked ourselves at this stage was the following:
Are most of the detailed tasks that comprise a work activity exposed to AI?
For example, the job activity "performing for or working directly with the public" is ambiguous on the surface. But consider the list of detailed tasks that comprise this broad activity:

Audition for roles

Perform for recordings

Perform music for the public

Collaborate with others to prepare or perform artistic productions

Entertain public with comedic or dramatic performances

Perform dances

Operate gaming equipment

Conduct amusement or gaming activities

Respond to customer problems or complaints

Respond to customer inquiries

Answer customer questions about goods or services

Communicate with customers to resolve complaints or ensure satisfaction

Resolve customer complaints or problems

Correspond with customers to answer questions or resolve complaints

The consensus we reached was that most of these detailed tasks, such as interfacing with customers or creating music, had a high degree of exposure to AI. Only a few tasks - auditioning, comedic or dramatic performances and dancing - were considered to have relatively low exposure to AI. For that reason, the broad activity "performing for or working directly with the public" is deemed to have high exposure to AI.

At the other end of the exposure scale is the work activity "coaching and developing others," entailing:

Coach others

Encourage patients during therapeutic activities

Visit individuals in their homes to provide support or information

Encourage students

Interact with patients to build rapport or provide emotional support

Support the professional development of others

Encourage patients or clients to develop life skills

The focus of most of these detailed tasks involves personal interaction. So, we judged that the activity "coaching and developing others" has low exposure to AI.

Overall, 16 work activities were assessed to have high exposure to AI, 16 more were judged to have medium exposure, and nine were deemed to have low exposure. (Refer to the methodology for where each activity was classified.)

Determining the level of exposure of a job to AI

The 41 work activities listed in O*NET are spread across all occupations in the O*NET database. That is to say, each occupation is a mix of low, medium and high AI-exposure activities. The question then is:
Which work activities are relatively more important in a job? Are high- or low-exposure activities more important than other activities?
To answer this, we first estimated the averages of the importance ratings for high-, medium- and low-exposure activities in each job, where the rating of each activity within a category is taken from the O*NET database. The rating for each activity ranges from one (not important) to five (extremely important).

Overall, among the 873 occupations we looked at, high-exposure activities were rated as being important to extremely important in 77% of occupations, and medium-exposure activities were similarly important in 72% of occupations. Low-exposure activities were rated as important in 39% of occupations. This suggests that high, medium and low exposure could simultaneously be important in a job.

The final step was to estimate the relative importance of high-, medium- or low-exposure activities in each job - that is, to determine which tasks are more important than the others in any given job. This procedure is described in the methodology. Occupations were then ranked two ways, once by the relative importance of high-exposure work activities and again by the relative importance of low-exposure work activities.

In our analysis, jobs that are most exposed to AI are in the top 25% of occupations ranked by the relative importance of high-exposure activities. Jobs that are least exposed to AI are in the top 25% of occupations ranked by the relative importance of low-exposure work activities. The other jobs may be thought of as having a medium level of exposure to AI. (Refer to the appendix for examples of occupations that are among the most or least exposed or have a medium level of exposure.)

To take an example, consider mechanical drafters, who prepare detailed working diagrams of machinery and mechanical devices. Mechanical drafters are among the workers most exposed to AI. For them, high-exposure activities have an average rating of 3.28 but low-exposure activities have an average rating of 2.36, where a rating of 3 means an activity is important.

For nannies, among the least exposed workers, high-exposure activities have an average rating of 2.36 but low-exposure activities have a rating of 3.03.

Previous research on the impact of AI on U.S. workers

Our analysis follows in the footsteps of other researchers who have recently examined the impact of AI on the workplace. Eloundou, Manning, Mishkin and Rock (March 2023) conclude that about one-in-five U.S. workers may see an impact on half or more of their job tasks. Felten, Raj and Seamans (April 2021) find that white-collar occupations requiring advanced degrees are most exposed to AI, as are industries providing financial or legal services. Webb (January 2020) reports that high-skill occupations, highly educated and older workers will be more impacted by AI, but he does not draw conclusions about the nature or the extent of the impact on workers. Our findings are broadly consistent with the results of these analyses.

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Which U.S. Workers Are More Exposed to AI on Their Jobs?

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In 2022, 19% of American workers were in jobs that are the most exposed to artificial intelligence, in which the most important activities may be either replaced or assisted by AI. Women, Asian, college-educated and higher-paid workers have more exposure to AI, but workers in the most exposed industries are more likely to say AI will help more than hurt them personally.

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Artificial Intelligence and Jobs: Evidence from Online Vacancies

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Artificial Intelligence and Jobs: Evidence from Online Vacancies

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Work, Workforce, Workers: Reinvented in the Age of Generative AI

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Artificial Intelligence and Algorithmic Fairness Initiative

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In 2021, U.S. Equal Employment Opportunity Commission (EEOC) Chair Charlotte A.

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AI Learning and Training Opportunities Database

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AI Learning and Training Opportunities Database

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DOE, in coordination with NSF, is creating a vibrant training and workforce ecosystem by advancing secure, trustworthy, and equitable AI for all Americans. This web portal highlights a non-exhaustive collection of DOE and NSF AI education, training, and workforce opportunities.

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The Partnership on AI was established to study and formulate best practices on AI technologies, to advance the public's understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.

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Digital Equity @ Work - National Skills Coalition

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The pandemic demonstrated the urgency of putting high-quality, connected technology in more hands. But it also made clear that we won't close the digital divide or realize our nation's economic potential until we empower all workers to adapt to technology's constant evolution in the workplace.

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Artificial Intelligence and Emerging Technology Initiative

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The Brookings Institution’s Artificial Intelligence and Emerging Technology Initiative aims to advance good governance of transformative new technologies.

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The Future of Work Initiative is a nonpartisan effort to strengthen the social contract in the midst of sweeping economic changes.

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AI4ALL opens doors to artificial intelligence for historically excluded talent through education and mentorship.

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The GitLab Foundation believes that innovative, ethical applications of AI, machine learning, and advanced data science have the promise to unlock significant opportunities to help people afford a better life. To encourage and enable this future, we are launching a new AI for Economic Opportunity gr

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The Future of Work & the Innovation Economy (FOWIE) Initiative conducts research, storytelling, and policy analysis to help policymakers, educators, organized labor, and employers ensure quality jobs and workforce pathways in emerging technology fields.

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AI Is Suddenly Everywhere, but Philanthropy Has Been Involved for Years. Here Are the Top Funders | Inside Philanthropy

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Artificial intelligence is on everyone’s mind now, but philanthropy has been boosting research or bracing for its implications for some time. Here are the major funders involved in the field.

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Digital tools and artificial intelligence are changing the face of learning and work. From computers, intelligent tutoring systems, AI chatbots (software applications used to simulate human-like chat conversations online), and question-answering digital teaching assistants to personalized learning programs using machine learning to adapt to each learner's current performance, AI is bringing about a new age of teaching and learning in workforce development contexts.

Beyond impacting the process of teaching and learning, machine learning in the workplace is also changing what capabilities individuals need to develop to be successful at work. At the Next Level Lab, we explore ways in which AI is changing how we think about learning and working, and consider how to support workers in developing uniquely human skills that will allow them to complement the work of machines rather than be replaced by them.

The following research briefs and blog posts from Next Level Lab researchers offer a range of ideas about how AI and intelligence augmentation are shaping the nature of work, as well as how emerging educational technologies can support learning in workforce development contexts:

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How AI and Other Emerging Technologies Are Changing the Workforce Development Landscape

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Digital tools and artificial intelligence are changing the face of learning and work.

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2023 is the year AI ate the internet, according to the New Yorker and anyone not living under a rock. AI has so deeply penetrated our society that we can't ignore the existential questions it raises, from the future of work to ethical dilemmas to questions about whether it will exacerbate bias and inequity or be used for the good of civil society.

While the for-profit sector is lightyears ahead of its nonprofit counterpart, readily adopting AI into their operations and tech stacks, the social sector is lagging woefully - but unsurprisingly - behind, given the resources needed to embark on such a transformation. Recognizing the need for a fundamental cultural shift, philanthropy has a responsibility to encourage, fund, and accelerate the adoption of responsible AI in and for the social sector.

We are all individually called to experiment and learn about AI, warts and all, but funding is the critical element to ensure that AI is harnessed for good. And we all know nonprofit infrastructure is perennially and abysmally under-funded. So, take this as a call-to-action, dear philanthropic partners, as your role is crucial in moving the sector forward and, equally as crucial, in helping with responsible AI development and adoption.

Making Inroads on Responsible AI Adoption

Despite the lack of funding, the sector presses forward. Nathan Chappell, an author and thought leader at the intersection of AI and philanthropy, and Mallory Erickson, a fundraising consultant and coach, among others, have created Fundraising.AI, a steering committee to help fundraisers promote the development and use of responsible artificial intelligence in their work. The Fundraising.AI team hosted a two-day virtual summit in October, covering topics ranging from the evolution of artificial intelligence to practitioner use cases and the ethical implications of incorporating AI into fundraising, and offering a much-needed infusion of visionary thinking on AI in the social sector.

Soon after the summit, the Technology Association of Grantmakers (TAG), in collaboration with Project Evident, released the " Responsible AI Adoption Framework " in December. The framework was developed to "guide grantmakers in the tactical and strategic adoption of AI." Underscoring the importance of "human-centered adoption," the framework outlines the considerations for incorporating AI into philanthropy in the areas of individual use, organizational efficiency, and mission attainment. Version one of a living, breathing framework, with input from over 300 grantmakers, TAG and Project Evident have provided a solid foundation upon which we can all build in 2024 and beyond.

Addressing the Digital Divide

We are now in the sixth wave of innovation, according to economists and historians who theorize that progress is marked by major technological advancements that lead to significant societal changes. But history tells us that waves of innovation, especially in the context of digital technologies, contribute to a digital divide. The benefits of technological advancements aren't often evenly distributed and tend to lead to economic disparities.

The digital divide is now a digital chasm. Fundraisers and grantmakers alike acknowledge that the playing field has undergone seismic shifts that require our attention. But the digital divide that has been growing at least since the Internet became commonplace is not an inevitable outgrowth of technological advancement.

At the December Salesforce World Tour NYC, Roy Lee, the associate dean of analytics and technology at NYU Stern School of Business, articulated the path forward. As critical as it is, the concept is often overlooked: Digital transformation will only be successful when champions, end-users, and powerful (resourced) decision-makers leverage the people, processes, and tools that we engage with every day. This is especially true with AI implementation and adoption.

This moment of accelerated innovation requires a holistic funding approach that considers not only the technological challenges but also the social and ethical implications. Philanthropy has a moral obligation to steward initiatives that address both the longstanding digital divide and emergent - and sometimes disruptive - technologies. It is not only an imperative for philanthropy but also an opportunity to harness AI for its own funding decisions and play a pivotal role in its development. It is much more effective to catch the wave than swim against the tide.

Ensuring a Sustainable AI Future

Turning back to the social sector: How are we - fundraisers, grantmakers, academics, nonprofit folks - going to ride this wave of innovation in service of social impact and civil society? These four crucial concepts serve as a possible framework to help philanthropy spearhead a collaborative approach to developing and adopting responsible AI.
1. Drive Sector-Level Vision
In philanthropy, we share a vision of contributing to something bigger than our individual selves, to help fund solutions to the world's wicked problems. We can do this not only by vigorously supporting the social sector in developing and adopting AI tools across the board, but by tapping into the expansive power of predictive and generative AI as funders. These tools have the potential to reshape the way we use historical data to inform our funding choices while simultaneously staying attuned to changing community needs. If developed and used responsibly, AI gives philanthropy a 360-degree vantage point, driving our sector-level vision of how we create and maximize positive social impact.
2. Fund the Back-End to Front-Load Impact
The foundational issues to AI adoption in the social sector are compounded by the scant resources (read: time and money) with which most nonprofits operate. And it is nonprofits, and by extension all of us in the global community, that stand to gain the most by adopting AI.

Philanthropy can and should play a crucial role in funding solutions to help the social sector and civil society access innovative technology, build capacity to manage its adoption and usage, and to share knowledge across the sector to improve the efficacy of the tools. The Patrick J. McGovern Foundation has set an example for philanthropy's role in funding AI solutions, recently announcing $66.4 million in awards to 148 organizations that are "leveraging AI and data science to address the world's most urgent and complex challenges."
3. Develop Guidelines
One of the top-rated sessions at the Fundraising.AI summit in October was a discussion with Microsoft Legal Counsel Cass Matthews, who introduced the importance of governance and guardrails as the essential starting point when introducing novel technological solutions. We will accelerate the responsible use of AI once we determine the rules of engagement and start building collaborative learning playing fields.

While the shared terms of engagement are yet to be defined, once guidelines are proposed and iteratively reviewed, it is philanthropy's responsibility to experiment with those boundaries and determine what works best. In fact, it is incumbent on all of us to experiment, play, and experiment more. We learn through play. And, incidentally, so does AI.
4. Invest in Equitable Innovation
We must also be aware of the transformative potential and inherent risks AI poses to vulnerable communities (Black, Indigenous, and other people of color, women, LGBTQ+ individuals, and others). One of the primary concerns is the risk of AI perpetuating systemic biases. For instance, facial recognition technology has higher error rates for people of color, leading to wrongful identifications and unfair surveillance. AI used in hiring processes to automatically scan and weed out applicants can also lead to algorithmic bias, potentially discriminating against legally protected candidates.

We must advocate for funding initiatives that rigorously audit AI systems for such biases and develop more equitable algorithms. This involves supporting research and advocacy that diversifies AI training data and promotes transparency in AI decision-making processes.

Beyond protection, philanthropy should also focus on supporting AI applications that directly benefit vulnerable communities. This includes investment in healthcare AI that accounts for diverse medical needs and historical health disparities, AI-driven environmental justice initiatives that help monitor and address pollution in marginalized areas, and AI tools that aid in equitable resource distribution in areas like education, housing, and business development.

As the entities who wield the most power in the social impact space - dollars equal power - philanthropy has a responsibility to thoughtfully address one of the biggest challenges that we will face in the history of our sector. How we act now will determine the future of our work. We are creating history, and while we may not like change, we will like irrelevance a whole lot less.
Rachel Kimber is a grantmaking strategist and speaker/consultant on digital strategies and technological solutions for social impact. Joanna Drew is an independent grant writing consultant and founder of Hilo Consulting. Ravit Dotan, Ph.D. is an AI ethics advisor, researcher, and speaker. Mark Greer II is co-executive director at the Transforming Power Fund.

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Responsible AI: How Philanthropy Can (and Should) Support the Movement | The Center for Effective Philanthropy

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Philanthropy has a responsibility to encourage, fund, and accelerate the adoption of responsible AI in and for the social sector.

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The emergence of text generating artificial intelligence applications such as ChatGPT could significantly change philanthropy — for better or worse.

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Have these machines gotten to the point that they’re starting to have something like human intelligence? Where they actually understand language and concepts, and can reason? David Kestenbaum talks with scientists at Microsoft who’ve been trying to figure that out.

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We’ve witnessed a revolution in A.I. since the public rollout of ChatGPT.  Our Senior Editor David Kestenbaum thinks that even though there’s been a ton of coverage, there’s one thing people haven’t talked much about: have these machines gotten to the point that they’re starting to have something like human intelligence? Where they actually understand language and concepts, and can reason? He talks with scientists at Microsoft who’ve been trying to figure that out.

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The first nationally representative survey in the U.S. on generative AI use at work and home.

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Generative AI is a groundbreaking technology, but its economic impact depends on the speed and intensity of its adoption. The paper The Rapid Adoption of Generative AI by Alexander Bick, Adam Blandin, and David Deming presents findings from the first nationally representative survey in the U.S. on generative AI use at work and home. The data are sourced from the Real-Time Population Survey (RPS), designed and weighted to be nationally representative and modeled after the CPS, a widely recognized