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Don't Expect Juniors to Teach Senior Professions to Use GenAI

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Don't Expect Juniors to Teach Senior Professions to Use GenAI

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Economic Policy Institute - A worker-centered approach to policy in the era of AI

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Economic Policy Institute - A worker-centered approach to policy in the era of AI

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A worker-centered approach to policy in the era of AI

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The deployment of AI in the workplace has shined a light on long-standing problems: The deployment of AI-powered technologies in the workplace has been associated with an array of worker experiences and outcomes that warrant concern, including discrimination, unsafe working conditions, loss of privacy, and seemingly arbitrary disciplinary action or discharge, among others. But these…

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CFPB Takes Action to Curb Unchecked Worker Surveillance

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CFPB Takes Action to Curb Unchecked Worker Surveillance

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Related article:

US agencies take stand against AI-driven employee monitoring | IAPP

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CFPB Takes Action to Curb Unchecked Worker Surveillance | Consumer Financial Protection Bureau

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The Consumer Financial Protection Bureau (CFPB) issued guidance to protect workers from unchecked digital tracking and opaque decision-making systems.

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Please keep this report confidential and do not share it outside of our workgroup.

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Draft Report - EO 24-01 Section 9 - Workgroup Members 10.23.24

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Instructions for Federal Agencies on AI Reporting

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Instructions for Federal Agencies on AI Reporting

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MOU to Federal Agencies about Agency Use of AI

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MOU to Federal Agencies about Agency Use of AI

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OECD, 2023. The impact of AI on the workplace: Evidence from OECD case studies of AI implementation

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OECD, 2023. The impact of AI on the workplace: Evidence from OECD case studies of AI implementation

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The impact of AI on the workplace: Evidence from OECD case studies of AI implementation

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How artificial intelligence (AI) will impact workplaces is a central question for the future of work, with potentially significant implications for jobs, productivity, and worker well-being. Yet, knowledge gaps remain in terms of how firms, workers,...

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International Labour Organization, 2023. GenAI and Jobs: A global analysis of potential effects on job quantity and quality

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International Labour Organization, 2023. GenAI and Jobs: A global analysis of potential effects on job quantity and quality

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IMF, 2023. Labor Market Exposure to AI: Cross-country Differences and Distributional Implications

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IMF, 2023. Labor Market Exposure to AI: Cross-country Differences and Distributional Implications

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Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

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Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

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UC Berkeley, 2023. Technology in the public sector and the future of government work

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UC Berkeley, 2023. Technology in the public sector and the future of government work

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This glossary provides definitions and explanations for some of the most common terms related to AI governance.

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Qualtrics, 2024. The 5 employee experience trends redefining work in 2024

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Qualtrics, 2024. The 5 employee experience trends redefining work in 2024

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Government Technology, 2024. Department of Labor Taps AI to Support Inclusive Hiring

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Government Technology, 2024. Department of Labor Taps AI to Support Inclusive Hiring

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The Department of Labor has published an AI and Inclusive Hiring Framework, which aims to inform and support inclusive practices when using artificial intelligence (AI) in hiring.

Organizations across the country are increasingly making the case for using AI technology in recruiting. Some experts argue that it can actually reduce biases in the hiring process; however, risks exist - including algorithmic bias. This new framework was published by the Partnership on Employment and Accessible Technology (PEAT). It aims to act as a tool to support employers in their use of AI in hiring, offering best practices to promote inclusive hiring and to increase benefits for job seekers with disabilities.

It offers 10 focus areas that employers can adopt in AI governance and hiring initiatives to promote disability-inclusive processes. The framework intends to support organizations, job seekers and workers, and others interested in advancing inclusive AI governance and practices.

Notably, this resource is based on the AI Risk Management Framework that the U.S. Department of Commerce's National Institute of Standards and Technology (NIST) released in January 2023. That guidance was created at the direction of Congress to help organizations identify and address risks of AI systems.

The new framework was developed through collaboration between the Department of Labor's Office of Disability Employment Policy (ODEP) and PEAT, in collaboration with NIST and other partners. To create it, these organizations leveraged input from disability advocates, AI experts, public- and private-sector experts, and the general public.

ODEP works with employers that understand the role of AI tools to improve inclusive hiring processes for people with disabilities, as Assistant Secretary of Labor for Disability Employment Policy Taryn Williams detailed in a statement: "The AI and Inclusive Hiring Framework published today charts a clear course for employers to navigate this transformation successfully."

The initiative aligns with several other federal government efforts. For example, the federal government and partners are working together to understand how AI at large can impact accessibility. Also, the October 2022 release of the Blueprint for an AI Bill of Rights - which some experts argue does not go far enough - promotes inclusive digital hiring practices for workers from marginalized and underserved communities.

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Department of Labor Taps AI to Support Inclusive Hiring

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With AI-powered recruitment tools increasingly used in hiring, the U.S. Department of Labor has announced the release of its own AI and Inclusive Hiring Framework to support inclusive hiring practices.

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SHRM, 2024. ADA at 34: AI's Promise and Challenge

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SHRM, 2024. ADA at 34: AI's Promise and Challenge

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Much progress has been made in the 34 years following the passage of the Americans with Disabilities Act (ADA) in 1990, but there’s room for more inclusion in U.S. workplaces for people with disabilities. AI can help.

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Route Fifty, 2024. Making AI Work for Government

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Route Fifty, 2024. Making AI Work for Government

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Deloitte, 2024. DEI and AI: Building systems without bias. 

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Deloitte, 2024. DEI and AI: Building systems without bias. 

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How chief DEI officers can lead equitable AI

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Chief DEI Officers are uniquely positioned to combine the best of DEI and AI. Learn how organizations can make an impact on equitable AI.

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Some 50 million Americans are neurodivergent.

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Navigating AI Adoption with ADKAR Slides

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Government Technology, 2024. New Washington, D.C., Tool Uses GenAI to Make Data Accessible

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Government Technology, 2024. New Washington, D.C., Tool Uses GenAI to Make Data Accessible

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New Washington, D.C., Tool Uses Generative AI to Make Data Accessible

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DC Compass, a new GenAI-based tool launched by the district in partnership with Esri, offers users answers to data-related questions. It is now available in a public beta version to improve its speed and accuracy.

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Government Technology, 2024. The District of Columbia Unveils an AI-Powered Career Platform

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Government Technology, 2024. The District of Columbia Unveils an AI-Powered Career Platform

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The District of Columbia Unveils an AI-Powered Career Platform

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Career Ready DC, a new AI-matching technology platform from the DC Department of Employment Services and Eightfold AI, aims to help match residents with employment opportunities more efficiently and equitably.

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Government Technology, 2024. AI in Hiring: Can It Reduce Bias?

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Government Technology, 2024. AI in Hiring: Can It Reduce Bias?

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There are risks in the use of artificial intelligence (AI) in hiring, but some experts argue AI technologies can actually reduce biases in the process.

AI has powered hiring behind the scenes for years; and while some say this use case can be a harmful one, others make the case that it can improve the process - potentially eliminating bias and directing a focus toward candidates' skills.

Career Ready DC, an AI-powered career platform launched in December by the Washington, D.C. Department of Employment Services (DOES), aims to do exactly that. In announcing the platform, D.C. Mayor Muriel Bowser emphasized the technology could help make the job search process more equitable.

The platform uses algorithms to highlight job matches, removing language that could perpetuate bias and instead promoting a person's skill set. The goal is to level the playing field for all people to equitably compete for job openings.

"For so long, in marginalized communities, technology hasn't really been used to innovate or advance services," DOES Director Unique Morris-Hughes told Government Technology. "And so, what Career Ready DC provides is another tool to eradicate bias."

The platform's launch came through a partnership between DOES and Eightfold AI, which helps organizations address workforce challenges with AI. Dan Hopkins, the company's vice president of global public sector and applied AI, told GT the use of AI in something as potentially sensitive as hiring is a "critically important topic," noting that scrutiny of use in this area is a good thing as it helps ensure AI will be used as "a source of good."

What sets Eightfold AI apart, Hopkins said, is that it helps organizations implement a skills-based approach to hiring. The approach uses AI to understand skills revealed both by a job seeker's previous roles and education, as well as potential skills based on job trajectory information. The platform then matches those candidates to employers.

Hopkins said the tool allows the humans that are part of the hiring process to leverage profile masking - seeing skills rather than factors like name, gender and race - to focus on skill-related data and obfuscate data points that could lead to biased decision-making.

"So, you're still having humans make those decisions, but you're giving them a more informed data set to make the very best decision," he said.

This, he said, will improve the process for job seekers as well, who often interpret skills requirements through vague job descriptions. Hopkins cited a Harvard study that found women don't apply to jobs unless they meet all qualifications, which means diverse and qualified candidates often filter themselves out.

Organizations using Eightfold AI software can access dashboards and reports to understand trends - or even drop-off points - during the hiring process, he said. For example, if female candidates are dropping off after on-site interviews, the organization can identify that trend and take action to course correct.

DOES is exploring other tech-related, resident-facing tools to further support job seekers in the process, including a forthcoming training provider scorecard so those looking to upskill can get data about training providers.

And across the nation, the trend of skills-based hiring is gaining ground, with many government organizations expanding hiring channels. Hopkins referenced an executive order on hiring under the Trump administration; and an executive order on diversity, equity, inclusion and accessibility under the Biden administration as two actions largely driving this shift: "And this is where artificial intelligence can really be a game-changer."

Eightfold AI is not the only tool being used in this space. HireVue is another technology company aiming to improve the hiring process for organizations.

The company recently released a report revealing that 66 percent of the employers surveyed are using AI-powered skills assessments as part of the hiring process. With or without AI, the report found 96 percent of respondents are increasing their focus on skills.

Juan Salazar, VP of public sector at HireVue, told GT via email that the mitigation of human bias in hiring is one of the primary benefits of using AI.

Salazar said HireVue helps public-sector organizations evaluate candidates based on skills, potential and interests, rather than unnecessary educational and credential requirements. With HireVue's technology, name, gender, age and ethnicity are never used to assess candidates.

"HireVue customers often see improvements in diversity even when they're not prioritizing that as an outcome," he wrote.

But Salazar also said there is a need for greater regulation in the area of AI in hiring - something he said the company supports. While he argued that AI can have benefits in the hiring process, he specified the importance of using "appropriately trained and audited algorithms."

Based on its potential, Hopkins noted AI is gaining popularity in this space; but he, too, urged caution in its development.

"When you ... train your AI off of your data set, any bias that is part of your organization is going to be re-perpetuated in the outcomes of the new AI tool," he said, noting the value of integrating a more expansive, external data set in addition to that of an organization.

Morris-Hughes, the DOES director, said department case managers will monitor Career Ready DC, in ongoing collaboration with a DOES team of IT professionals, to ensure data integrity and that the platform is being used as intended. She also cited Bowser's February actions to ensure the responsible use of AI in the District of Columbia, explaining that the mayor's order outlines both procedures and values for responsible - and equitable - AI implementation.

However, the platform's launch took place prior to Bowser's order. As such, Morris-Hughes underlined the importance of pre-work that the DOES team did to ensure the tools the department was investing in allowed transparency, security and equity. Morris-Hughes said the department had gained essential experience investing in new tech tools from its role running the unemployment insurance system during the COVID-19 pandemic.

According to Morris-Hughes, support from city leaders provided the crucial foundation for the platform's creation, and she credited the district's centering of equity in its vision and values. She urged other government organizations that are exploring similar AI uses to work with their CIO to do so.

"I'm not afraid; I'm encouraged by the use of technology and AI, and how we can use technology for marginalized communities - to eradicate poverty, to level the playing field, to get people family-sustaining jobs," Morris-Hughes said. "It's actually something to be quite excited about."

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As governments increasingly fold artificial intelligence into their hiring processes, the question emerges: Does AI increase or reduce bias? Some experts argue that, when implemented responsibly, it reduces bias and directs a focus on skills.

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McKinsey & Company, 2024. The true GenAI talent pool

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McKinsey & Company, 2024. The true GenAI talent pool

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The pool of generative AI (gen AI) talent within organizations is likely bigger than expected.

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NTIA, 2024. AI Accountability Policy Report. 

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NTIA, 2024. AI Accountability Policy Report. 

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Executive Summary

Artificial intelligence (AI) systems are rapidly becoming part of the fabric of everyday American life. From customer service to image generation to manufacturing, AI systems are everywhere.

Alongside their transformative potential for good, AI systems also pose risks of harm. These risks include inaccurate or false outputs; unlawful discriminatory algorithmic decision making; destruction of jobs and the dignity of work; and compromised privacy, safety, and security. Given their influence and ubiquity, these systems must be subject to security and operational mechanisms that mitigate risk and warrant stakeholder trust that they will not cause harm.

Commenters emphasized how AI accountability policies and mechanisms can play a key part in getting the best out of this technology. Participants in the AI ecosystem - including policymakers, industry, civil society, workers, researchers, and impacted community members - should be empowered to expose problems and potential risks, and to hold responsible entities to account.

AI system developers and deployers should have mechanisms in place to prioritize the safety and well-being of people and the environment and show that their AI systems work as intended and benignly. Implementation of accountability policies can contribute to the development of a robust, innovative, and informed AI marketplace, where purchasers of AI systems know what they are buying, users know what they are consuming, and subjects of AI systems - workers, communities, and the public - know how systems are being implemented. Transparency in the marketplace allows companies to compete on measures of safety and trustworthiness, and helps to ensure that AI is not deployed in harmful ways. Such competition, facilitated by information, encourages not just compliance with a minimum baseline but also continual improvement over time.

To promote innovation and adoption of trustworthy AI, we need to incentivize and support pre- and post-release evaluation of AI systems, and require more information about them as appropriate. Robust evaluation of AI capabilities, risks, and fitness for purpose is still an emerging field. To achieve real accountability and harness all of AI's benefits, the United States - and the world - needs new and more widely available accountability tools and information, an ecosystem of independent AI system evaluation, and consequences for those who fail to deliver on commitments or manage risks properly.

Access to information by appropriate means and parties is important throughout the AI lifecycle, from early development of a model to deployment and successive uses, as recognized in federal government efforts already underway pursuant to President Biden's Executive Order Number 14110 on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence of October 30, 2023 ("AI EO"). This information flow should include documentation about AI system models, architecture, data, performance, limitations, appropriate use, and testing. AI system information should be disclosed in a form fit for the relevant audience, including in plain language. There should be appropriate third-party access to AI system components and processes to promote sufficient actionable understanding of machine learning models.

Independent evaluation, by appropriate means and parties is important throughout the AI lifecycle, from early development of a model to deployment and successive uses, as recognized in federal government efforts already underway pursuant to President Biden's Executive Order Number 14110 on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence of October 30, 2023 ("AI EO"). This information flow should include documentation about AI system models, architecture, data, performance, limitations, appropriate use, and testing. AI system information should be disclosed in a form fit for the relevant audience, including in plain language. There should be appropriate third-party access to AI system components and processes to promote sufficient actionable understanding of machine learning models.

Consequences for responsible parties, building on information sharing and independent evaluations, will require the application and/or development of levers - such as regulation, market pressures, and/or legal liability - to hold AI entities accountable for imposing unacceptable risks or making unfounded claims.

The AI Accountability Policy Report conceives of accountability as a chain of inputs linked to consequences. It focuses on how information flow (documentation, disclosures, and access) supports independent evaluations (including red-teaming and audits), which in turn feed into consequences (including liability and regulation) to create accountability. It concludes with recommendations for federal government action, some of which elaborate on themes in the AI EO, to encourage and possibly require accountability inputs.

In April 2023, the National Telecommunications and Information Administration (NTIA) released a Request for Comment ("RFC") on a range of questions surrounding AI accountability policy. The RFC elicited more than 1,400 distinct comments from a broad range of stakeholders. In addition, we have met with many interested parties and participated in and reviewed publicly available discussions focused on the issues raised by the RFC.

Based on this input, we have derived eight major policy recommendations, grouped into three categories: Guidance, Support, and Regulations. Some of these recommendations incorporate and build on the work of the National Institute of Standards and Technology (NIST) on AI risk management. We also propose building federal government regulatory and oversight capacity to conduct critical evaluations of AI systems and to help grow the AI accountability ecosystem.

While some recommendations are closely linked to others, policymakers should not hesitate to consider them independently. Each would contribute to the AI accountability ecosystem and mitigate the risks posed by accelerating AI system deployment. We believe that providing targeted guidance, support, and regulations will foster an ecosystem in which AI developers and deployers can be properly held accountable, incentivizing the appropriate management of risk and the creation of more trustworthy AI systems.

GUIDANCE

1. Audits and auditors: Federal government agencies should work with stakeholders as appropriate to create guidelines for AI audits and auditors, using existing and/or new authorities. This includes NIST's tasks under the AI EO concerning AI testing and evaluation and other efforts in the federal government to refine guidance on such matters as the design of audits, the subject matter to be audited, evaluation standards for audits, and certification standards for auditors.

2. Disclosure and access: Federal government agencies should work with stakeholders to improve standard information disclosures, using existing and/or new authorities. Greater transparency about, for example, AI system models, architecture, training data, input and output data, performance, limitations, appropriate use, and testing should be provided to relevant audiences, including in some cases to the public via model or system cards, datasheets, and/or AI "nutrition labels." Standardization of accessible formats and the use of plain language can enhance the comparability and legibility of disclosures. Legislation is not necessary for this activity to advance, but it could accelerate it.

3. Liability rules and standards: Federal government agencies should work with stakeholders to make recommendations about applying existing liability rules and standards to AI systems and, as needed, supplementing them. This would help in determining who is responsible and held accountable for AI system harms throughout the value chain.

SUPPORT

4. People and tools: Federal government agencies should support and invest in technical infrastructure, AI system access tools, personnel, and international standards work to invigorate the accountability ecosystem. This means building the resources necessary, through existing and new capacity, to meet the national need for independent evaluations of AI systems, including:

Datasets to test for equity, efficacy, and other attributes and objectives;

Computing and cloud infrastructure required to conduct rigorous evaluations;

Legislative establishment and funding of a National AI Research Resource;

Appropriate access to AI systems and their components for researchers, evaluators, and regulators, subject to intellectual property, data privacy, and security- and safety-informed protections;

Independent evaluation and red-teaming support, such as through prizes, bounties, and research support;

Workforce development;

Federal personnel with the appropriate sociotechnical expertise to design, conduct, and review evaluations; and

International standards development (including broad stakeholder participation).

5. Research: Federal government agencies should conduct and support more research and development related to AI testing and evaluation, tools facilitating access to AI systems for research and evaluation, and provenance technologies, through existing and new capacity. This investment would move towards creating reliable and widely applicable tools to assess when AI systems are being used, on what materials they were trained, and the capabilities and limitations they exhibit. The establishment of the U.S. AI Safety Institute at NIST in February 2024 is an important step in this direction.

REGULATORY REQUIREMENTS

6. Audits and other independent evaluations: Federal agencies should use existing and/or new authorities to require as needed independent evaluations and regulatory inspections of high-risk AI model classes and systems. AI systems deemed to present a high risk of harming rights or safety - according to holistic assessments tailored to deployment and use contexts - should in some circumstances be subject to mandatory independent evaluation and/or certification. For some models and systems, that process should take place both before release or deployment, as is already the case in some sectors, and on an ongoing basis. To perform these assessments, agencies may need to require other accountability inputs, including documentation and disclosure relating to systems and models. Some government agencies already have authorities to establish risk categories and require independent evaluations and/or other accountability measures, while others may need new authorities.

7. Cross-sectoral governmental capacity: The federal government should strengthen its capacity to address cross-sectoral risks and practices related to AI. Whether located in existing agencies or new bodies, there should be horizontal capacity in government to develop common baseline requirements and best practices, and otherwise support the work of agencies. These cross-sectoral tasks could include:

Maintaining registries of high-risk AI deployments, AI adverse incidents, and AI system audits;

With respect to audit standards and/or auditor certifications, advocating for the needs of federal agencies and coordinating with audit processes undertaken or required by federal agencies themselves; and

Providing evaluation, certification, documentation, coordination, and disclosure oversight, as needed.

8. Contracting: The federal government should require that government suppliers, contractors, and grantees adopt sound AI governance and assurance practices for AI used in connection with the contract or grant, including using AI standards and risk management practices recognized by federal agencies, as applicable. This would ensure that entities contracting with the federal government or receiving federal grants are enacting sound internal AI system assurances. Such practices in this market segment could accelerate adoption more broadly and improve the AI accountability ecosystem throughout the economy.

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Earned Trust through AI System AssuranceNTIA.govArtificial IntelligenceAI Accountability Policy ReportOverviewRequisites for AI Accountabilit...

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Workday, 2024. AI IQ: Insights on AI in the Enterprise

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Workday, 2024. AI IQ: Insights on AI in the Enterprise

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AI IQ: AI and Machine Learning in the Enterprise

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New data says most consider AI and ML key to better operations and business transformation—but full implementation is out of reach. This report on AI goes into more detail: global AI perspectives; top challenges and how to address them; and how to achieve less artificial, more intelligent AI.

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Deloitte, 2024. Now decides next: Getting real about GenAI. 

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Deloitte, 2024. Now decides next: Getting real about GenAI. 

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International Monetary Fund, 2024, GenAI: Artificial Intelligence and the Future of Work

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International Monetary Fund, 2024, GenAI: Artificial Intelligence and the Future of Work

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International Monetary Fund, 2024. GenAI and the Future of Work

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Qualtrics 2024 Employee Experience Trends

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Qualtrics 2024 Employee Experience Trends

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Qualtrics 2024 Employee Experience Trends

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U.S. DOL's AI & Inclusive Hiring Framework

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U.S. DOL's AI & Inclusive Hiring Framework

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Department of Labor Taps AI to Support Inclusive Hiring

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With AI-powered recruitment tools increasingly used in hiring, the U.S. Department of Labor has announced the release of its own AI and Inclusive Hiring Framework to support inclusive hiring practices.

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California's AI Training for state employees

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California's AI Training for state employees

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California Offers Training in AI Use for Public Employees

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The California Department of Human Resources has released professional development courses in generative artificial intelligence. The classes were created with InnovateUS, which worked with New Jersey on similar courses.

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New Jersey's AI Training for state employees

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New Jersey's AI Training for state employees

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N.J. Unveils AI Training, Tool for State Government Workers

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The state has been an early adopter of artificial intelligence, and is now equipping staff with the skills and knowledge they need to leverage AI securely. Training is free and voluntary.

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Maryland's free AI training for state employees

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Maryland's free AI training for state employees

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The Maryland Department of Information Technology (DoIT) has partnered with InnovateUS to offer free artificial intelligence training to state employees.

With the rise of AI, government leaders are taking a new role in understanding, governing and using the technology. In July, New Jersey started offering AI training - created in partnership with InnovateUS - to its state government workers, aiming to offer an overview of generative AI, best practices and strategies to reduce risk. In August, California started offering its own training. Now, Maryland joins a growing list of states educating its employees on AI use.

"By prioritizing best-in-class education, the state's workforce will be able to identify risks and opportunities more quickly and implement this technology effectively and responsibly," said DoIT Secretary and Chair of the AI Subcabinet Katie Savage in a statement.

The subcabinet was established earlier this year through a January executive order by Maryland Gov. Wes Moore. It helps to facilitate ethical AI use statewide by fostering partnerships and establishing objectives. The subcabinet is expected to release policies, playbooks and road maps later this year to guide AI use in the state.

This AI training opportunity in Maryland will build on the state's prior work in this space, including that of the subcabinet, to create "a culture of responsible innovation," as detailed in the announcement. DoIT is also working to create a public AI inventory of active AI use cases. In addition, DoIT is working with other agencies in the state to guide low-risk, AI-related pilot programs. According to the announcement, the new AI training will help to create an educated workforce, which "will be a cornerstone of the state's approach to GenAI use."

The InnovateUS training was created with the help of input from more than 100 stakeholders across the private, public and education sectors.

"It really was a massive collaborative effort to ensure that we were creating something that was really for public servants and that would be responsive to what people felt they wanted and needed," Beth Noveck previously told Government Technology. Noveck is the director of the Burnes Center for Social Change at Northeastern University and The Governance Lab. She is also New Jersey's first chief AI strategist.

While Maryland, New Jersey and California are all teaming on this with InnovateUS, it is not the only organization supporting AI training efforts. For example, in Oklahoma, a partnership with Google is enhancing AI literacy. At the local level, a National Association of Counties AI training program aims to equip county officials.

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Maryland to Offer Free AI Training to State Employees

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The Maryland Department of Information Technology is partnering with InnovateUS to offer free artificial intelligence training to state employees, joining a growing list of states to do so.

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This survey will help inform the impact assessment of generative artificial intelligence (GenAI) on the Washington state workforce required by Executive Order 24-01 Section 9. The intended audience of this survey is only EO 24-01 Section 9 work group members.

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Survey to Inform Impact Assessment of GenAI on the Washington State Workforce

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Survey to Inform Impact Assessment of GenAI on the Washington State Workforce

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EO 24-01 AI Work Group Meeting 9.5.24

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Washington State AI Task Force Labor Subcommittee Meeting 8.26.24

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Washington State AI Task Force Labor Subcommittee Meeting 8.26.24

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Overview:

This was the first meeting of a labor subcommittee focusing on the impact of AI in workplaces. The discussion includes introductions, participant backgrounds, potential concerns with AI, and policy suggestions. Participants represent various sectors, including labor unions, hospitality, technology, and government.

Key Themes:

1. Scope and Impact of AI:

- AI is increasingly integrated into workplaces, affecting various industries.

- There is a need to differentiate between "labor" (unionized workforce) and "employment" (wages, overtime, hiring, discrimination).

2. Concerns with AI in the Workplace:

- Job Changes: AI may not fully replace jobs but will change job functions, leading to concerns about job security and skill requirements.

- Worker Involvement: It is important to include workers in the design of AI systems to ensure they augment rather than replace human labor.

- Automated HR Functions: AI is already being used for HR tasks like tracking work hours and performance, which can lead to wage theft, discrimination, and management issues.

- Work Intensification: AI might result in increased expectations for speed and efficiency, creating a higher workload for employees.

- Bias and Surveillance: AI-driven systems, like facial recognition, can perpetuate bias and increase surveillance and monitoring, especially in gig work.

3. Desired Policy Directions:

- There is a call for legislation to safeguard worker data, limit AI monitoring, and prevent wage theft or job losses due to automation.

- Advocating for responsible AI use, with standards for safety, worker well-being, and equitable wage structures.

- Emphasizing training and reskilling programs, particularly for underrepresented groups, to adapt to the evolving workplace.

- Suggesting worker-centered AI applications, such as monitoring workplace safety to prevent injuries.

4. AI in Different Contexts:

- Participants discussed various AI applications in hospitality, agriculture, and technology, highlighting the diverse ways AI is being used, such as chatbots, automated scheduling, and decision-making.

- Some AI uses, such as automated customer service, are more visible, while others, like internal HR decision-making systems, are less obvious to workers and consumers.

5. Legislation and Best Practices:

- There was a mention of tracking AI-related bills in different jurisdictions and looking at international frameworks like the EU's AI Act.

- Participants expressed a desire to learn from other states' efforts and to focus on building effective regulations that protect workers without overburdening small businesses.

6. Next Steps:

- The group discussed the importance of creating a structured agenda for future meetings.

- Plans for recurring meetings to discuss ongoing concerns and possible legislation.

- Participants emphasized the need for collaboration and setting guardrails to ensure AI benefits workers. The discussion concluded with a call for structured approaches in future meetings to dive deeper into specific issues and action points.

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The AI Task Force Labor Subcommittee convenes for a virtual meeting.

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Offered by DeepLearning.AI. Instructed by AI pioneer Andrew Ng, Generative AI for Everyone offers his unique perspective on empowering you ... Enroll for free.

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Leaders from government, Congress, nonprofits, and industry to discuss transformative national capabilities and potential AI advancements.

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With President Biden's "Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence", Office of Management and Budget...

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Federal Government Use Case Inventory of AI

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Federal Government Use Case Inventory of AI

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Use cases as of September 1, 2023.

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The United States stands to benefit significantly from harnessing the opportunities of AI to improve government services. The federal government is leveraging AI to better serve the public across a wide array of use cases, including in healthcare, transportation, the environment, and benefits delivery. The federal government is also establishing strong guardrails to ensure its use of AI keeps people safe and doesn't violate their rights.

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The federal government is harnessing the opportunities of AI to improve its services for the public.

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Win with AI: Lead the Human Side of Change - Prosci

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Win with AI: Lead the Human Side of Change - Prosci

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Draft EO 24-01 Section 9 - Final Outline

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OFM received approval from WaTech and UC Berkeley to share these survey questions with the EO 24-01 Section 9 work group members. I’ve included a disclaimer at the beginning of the survey questions, so if you do end up administering this survey, please include this disclaimer in its distribution.

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WaTech and UC Berkeley Survey Questions - To Share

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Half of Public Thinks Generative AI 'Important' For Improving Government Services

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Half of Public Thinks Generative AI 'Important' For Improving Government Services

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In a recent survey of the public, just over half of respondents said they believe the use of generative artificial intelligence "is important" for improving the services delivered by government agencies, such as health care benefits and motor vehicle services.

The results published Thursday follow an annual survey conducted last April by the accounting firm KPMG, in which 1,100 adults living in the United States were asked about personal finance, the economy and attitudes on the economy and government. Only 30% said they believed government agencies were adequately using technology to improve services.

Even fewer respondents in big cities said their local governments were using technology adequately: 24% in Boston and 23% in the San Francisco Bay Area.

New Yorkers responded more positively than the national average, with a 34% approval rate. New Yorkers also reported more hopeful attitudes about generative AI than the national average. In New York, 60% said motor vehicle department services, health care benefits and managing consumer complaints could be improved by generative AI.

When asked about cybersecurity, 54% said they were more concerned about potential incidents compromising their data at government agencies, as opposed to at private companies.

The survey also revealed generational differences: 46% of Generation Z and 37% of millennials said government agencies were adequately using technology to improve their interactions with the public, whereas just 23% of Generation X and 25% of baby boomers felt similarly.

"Embracing technology is no longer an option, but a vital imperative for government agencies," KPMG executive Lorna Stark said in the survey report. "In an era of constant change and increasing constituent expectations, leveraging technology is the key to delivering efficient and effective public services. By embracing emerging technologies such as GenAI, agencies can enhance transparency, streamline operations and improve constituent engagement."

Written by Keely Quinlan

Keely Quinlan reports on privacy and digital government for StateScoop. She was an investigative news reporter with Clarksville Now in Tennessee, where she resides, and her coverage included local crimes, courts, public education and public health. Her work has appeared in Teen Vogue, Stereogum and other outlets. She earned her bachelor's in journalism and master's in social and cultural analysis from New York University.

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Half of public thinks generative AI 'important' for improving government services | StateScoop

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The 2024 annual survey by KPMG found that the public's attitudes about generative AI use in government varies by location and age group.

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Technological Change in Five Industries: Threats to Jobs, Wages, and Working Conditions

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Technological Change in Five Industries: Threats to Jobs, Wages, and Working Conditions

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Findings

I. Employers' use of new technologies is changing the content of workers' jobs, but is likely to have limited effects on the overall number of jobs

Each team of researchers identified various new technologies that employers are using in each industry. These ranged from relatively simple improvements in internet and communications technology and digitization of information to complex data collection and algorithmic technologies, including but not limited to autonomous machines. Across the industries we studied, our researchers found that employers' use of new technologies is altering both the content and the processes involved in people's jobs in a range of ways.

Employers' use of new technologies may cause job reductions in some specific occupations, but is not expected to lead to an overall reduction in the number of jobs in the industries studied.

Across industries, however, our researchers did not find that widespread technology-induced job loss was currently happening, or that it was likely to happen. In some cases, a growing demand for services-due to changing consumer preferences (e.g., e-commerce) or changing demographics (e.g., an aging population requiring more healthcare services)-has led to a demand for more workers. In these circumstances, overall demand for certain occupations of workers is outpacing technology-induced job loss or slower job growth. Other factors contributing to worker shortages in specific industries-such as long-term wage stagnation for truck drivers-may result in labor demand for workers continuing to outpace supply, even as automation reduces the overall number of jobs.

Another reason that large-scale job displacement is unlikely in the foreseeable future is that the technology to automate certain types of tasks, such as item-picking to package e-commerce orders or delivery vehicles navigating complex urban environments, remains inadequate for employers' needs. And even where technology exists that is capable of automating certain tasks, there may be other reasons employers prefer a person to a machine in a particular role. For instance, employers may prefer a human worker to answer a customer's questions about a product in a store, or to identify an appropriate place to leave a package at someone's residence.

Employers are using technologies in ways that create new tasks and jobs, and shift the allocation of tasks across machines, workers, firms, and customers.

Understanding how new technologies are changing some workers' jobs requires an examination of the ways in which technologies affect the specific tasks the jobs involve. Every occupation consists of an assemblage of tasks, which often vary across industries or employers, and can change for all kinds of reasons, including employers' adoption of new technologies.

Some tasks are easier than others to execute using technology, such as those that involve highly structured and repetitive actions. Even so, employers rarely use technologies to replace all tasks involved in a worker's job; technologies are more often used to substitute for specific tasks, leaving other aspects of the job intact but potentially transformed. For example, where employers use automated chatbots to field customer service calls, human representatives may end up fielding more complicated inquiries, while the simpler ones are resolved by machines.

Technology-enabled task reorganization can create new tasks, such as remote operation of semi-autonomous vehicles, maintenance and programming of new machinery and software, and managing and staffing order-picking and curbside pickup for retail and grocery stores. This may create new jobs, increase the demand for certain jobs, or change the scope of work involved in a job.

Employers may also use technologies in ways that redistribute tasks among different workers. These could be workers within a particular firm, or workers employed in other firms, or independent contractors. In some grocery stores, retrieving and packing groceries for online orders is carried out by employees of the store, but in other cases (and sometimes within the same store) these tasks are carried out by a worker employed or contracted by a third party, such as Instacart. Similarly, restaurant take-out deliveries may be completed by restaurant employees, or by workers affiliated with a third-party ordering platform like Doordash or UberEats.

Employers' use of technologies may contribute to broader shifts in employment as well, across industries or market segments. The increased reliance on e-commerce for retail sales, for instance, has reduced demand for employment in stores, but increased the need for workers in warehouses and trucking. It has also shifted unpaid work by consumers in shopping and driving to paid work in order fulfillment and home delivery services.

Employers are using technologies in ways that change how job tasks are done.

In addition to the reshuffling of tasks, employers are using new technologies in ways that change the processes involved in workers' job tasks. Advances in data collection and algorithmic technologies are enabling employers to make changes to a broad array of HR and management functions, such as hiring, scheduling, task direction and pacing, monitoring, evaluation, and discipline or dismissal. These changes serve specific functions; here, we describe several types of managerial objectives, offer examples of the technologies that employers are using toward those ends, and discuss how these technologies change the way workers complete their tasks.

Safety: Some employers are using technologies intended to make work safer; examples include machinery that can alleviate the need for heavy lifting and sensors that track driving conditions, vehicle operations and potential safety hazards. These technologies can change the physical processes involved in workers' tasks, or give workers real time notifications to adjust their actions.

Personnel decision-making: In some cases, employers use data-gathering and analytic processing software to compile and interpret information about current or prospective employees, which human managers then take into consideration in their decisions about personnel. In other cases, employers are using algorithmic technologies to replace certain aspects of human decision-making altogether.

Information-sharing: Employers in many industries are using technologies that can facilitate communication and information-sharing, which can affect work processes in a wide variety of ways, such as changing the order in which workers complete their tasks, and giving them real-time direction and feedback. A few of the far-reaching range of examples of these types of technologies include delivery apps, customer service platforms, telehealth, and electronic health records.

Pace-setting: In several of the industries our researchers studied, employers are using technologies aimed at speeding up work processes to reduce costs. For instance, warehouses and retail stores may use digital inventory tracking systems and stocking devices to help streamline workers' movement of goods in a facility. Delivery drivers often use mapping and route-planning software intended to optimize speed and maximize their number of deliveries.

Monitoring: Employers are also using technologies to monitor and track workers' locations, activities, and the accuracy of their work. This type of tech is increasingly common in warehouses and delivery, but is also being deployed in home health care, where employers are using phone-based software to manage workers remotely by tracking their location and task-completion at clients' residences.

The managerial objectives highlighted here are not exhaustive, nor are they mutually exclusive. Employers adopt technologies and make other changes to production processes for a complex array of reasons. These changes can affect the processes involved in workers' jobs in intended and unintended ways, and can have both positive and negative effects on job quality. In the next section, we discuss the consequences of employers' use of new technologies for workers.

II. Employers in each industry are using new technologies in ways that may degrade workers' wages and working conditions and worsen existing labor market inequities

Employers can use new technologies in ways that are helpful to workers, as some of the above examples indicate. Technologies can make work safer, and reduce worker time spent on paperwork or arduous, repetitive tasks. However, in each industry, our researchers concluded that many employers are likely to use new technologies in ways that threaten workers' wages and job quality.

Employers' use of technology to reorganize work and production could lead to lower wages, deskilling, and deteriorating job quality for workers.

When employers reorganize and reshape production processes, the changes they make can have consequences for workers' wages and job quality. While the consequences aren't invariably or inevitably negative, our researchers found that technologically-induced task redistribution may result in poor outcomes for many workers.

Technologically-induced reorganization of production processes has also resulted in work being shifted within industries in ways that favor lower-wage segments. In trucking, the jobs most at risk of displacement from automation are long-distance truck driving jobs, which have higher rates of pay compared to local delivery jobs; the latter increased in number as e-commerce has grown but are much harder to automate.

Employers' use of technology to change work processes can lead to work speed-up accompanied by decreased autonomy and privacy for workers.

The intensive surveillance and grueling pace at Amazon warehouses have been well-documented in media accounts and have been cited as an important impetus to attempts to organize warehouse workers into unions. Workers report that the conditions in Amazon warehouses take a serious mental and physical toll, leading to exhaustion, workplace injuries, and psychological stress induced by the pressure to achieve their required productivity rates.

In trucking, drivers can be exposed to an extreme amount of data collection and electronic monitoring. Employers use sensors and real-time visual data streams to track and assess everything happening inside or around the vehicle, including location, possible hazards, driving patterns (speed, acceleration, braking, etc.), and driver behavior (seatbelt use, driver fatigue, or other distractions like texting or eating). Fleet managers can use these systems to "exert control over workers by setting quantified metrics to evaluate driver performance and challenge workers' accounts of driving conditions."

Employers are using new technologies in ways that can worsen existing labor market inequities.

Certain new technologies have biased outcomes for women, people of color, and other disadvantaged workers. For example, productivity-based pay structures to incentivize speed in warehouse work creates additional challenges for workers who are not as strong or physically fit, such as older workers. Additionally, algorithms used in applicant screening and hiring processes are notorious for replicating and exacerbating existing patterns of discrimination in the labor market. Research also indicates that technologies like speech and facial recognition can be less accurate in identifying and communicating with women, people of color, and people whose first language is not English. This can create additional barriers for workers who must interact with these systems in their jobs.

The work of our researchers and others suggests that the most serious repercussions of new technologies in the workplace will fall on those workers already facing the greatest structural challenges in the labor market. The use of new technologies will worsen existing inequities.

III. Industry context shapes employer decision-making about new technologies, leading to variation in adoption and worker impacts

A defining feature of each of our researchers' projects was the recognition that industry context shapes technology adoption decisions. Across and within industries, our researchers observed variation in the pace of employers' uptake of new technologies, their goals with respect to technology, and the consequences of these changes for workers. The factors that had the biggest impact on technology adoption fell into five categories: regulations, worker organizations, competitive structures, industry trends, and broader societal and market trends. These factors shaped employers' understanding of how they might use new technologies in the context of their other objectives and priorities. In some cases, these factors accelerated technological adoption and in others they hindered it.

Regulations

Regulations establish the ground rules that shape competitive market strategies, distinct market segments, and the distribution of power across market actors. Variation in regulation by industry or by region therefore plays a formative role in shaping employers' technology adoption decisions.

The health care industry, on the other hand, is highly regulated. Health care involves a complex web of actors and regulations, organized around a division between the providers of care (hospitals, etc.) and the financers of care (insurance companies and the government). In recent years, the government has tried to guide the market toward payment models based on "value-based care" (VBC) rather than "fee for service" care, which increase quality and efficiency of care provision. Under VBC models, insurers contract to reimburse providers on a fixed rate (e.g. per-member-per-month), so providers internalize the risk of variable costs associated with their patient population, and thus they no longer have an incentive to maximize the number of claims submitted to insurers.

Some states have stronger labor regulations, however, such as laws that specifically target independent contractor misclassification. Other states have laws that make it more difficult for workers to advocate for their rights and form unions. Twenty-eight states have "right to work" (RTW) laws that inhibit worker organizing. In RTW states, labor unions have less power to shape legislation or working conditions, and wages for workers tend to be lower. The strength or weakness of labor regulation affects workplace technology deployment in important ways; workers' ability to fully participate in this process can be inhibited or enhanced depending on the law, as can their ability to organize to protect themselves from potential harms of new technologies (see next section).

The lack of regulation governing employers' uses of technology in the workplace has had a profound effect on employers' use of technology in relation to their workers. As we note in our 2021 report Data and Algorithms at Work: The Case for Worker Technology Rights, the dearth of regulation creates strong incentives for employers to use digital technologies widely, and in ways that can directly or indirectly harm workers. There is also no oversight of the testing and quality of new systems that developers sell, which can worsen adverse effects for workers.

Recent efforts to remedy this challenge have been promising, however. In September 2021, California passed a bill targeting the grueling pace of work in Amazon warehouses by limiting companies' use of production quotas, and improving transparency around the use of algorithms in setting quotas, and several states have since passed or proposed similar legislation.[47] In April 2022, California legislators introduced A.B. 1651, The Workplace Technology Accountability Act, which would establish broad protections for workers against employers' use of monitoring and algorithmic management technologies. Legislators in California and elsewhere have passed laws fighting independent contractor misclassification, granting employment protections like minimum wage, overtime, and workers' compensation to workers in trucking and app-based delivery.[49]

Worker organizations

Unions also play an important role in certain segments of the trucking and grocery industries. The most recent contract negotiated between United Parcel Service (UPS) and the Teamsters union includes a requirement that UPS give the Teamsters six months' notice of any company plans to integrate emerging tech, such as drones, driverless vehicles, or truck platooning.[52] The United Food and Commercial Workers union (UFCW), which represents grocery workers, has fought to keep the growing numbers of e-commerce and delivery jobs in-house. UFCW has challenged employer attempts to outsource these jobs to third-party app-based shopping companies that typically use non-union workers or independent contractors.[54]

Worker organizations can also affect technology adoption by attempting to create policy. Where unions do not currently exist in significant numbers, other organizations representing workers-such as United for Respect in the retail sector, the Warehouse Workers Resource Center, and Gig Workers Rising-have been leading efforts to advocate for stronger regulations of industries around technology and other job quality issues. In some cases, worker organizations have partnered or allied themselves with unions.

Competitive structures

Regulations create a unique structure of competitive and cooperative arrangements between firms in each industry. Market structures and market power shape employers' strategies for growth and survival: what problems are employers trying to solve, what technologies could address those problems, and what factors will affect an employer's decision to invest?

Weak antitrust enforcement in the U.S. has allowed ownership consolidation to proliferate in many industries, which in turn has led to a gap between large, well-resourced industry leaders that are able to invest in and experiment with cutting-edge technologies, and smaller firms that tend to lag far behind. This dynamic is especially notable in retail and warehouses. Historically, employers in both industries have been slow to adopt new technologies. The rise of Amazon has reoriented the competitive terrain in both sectors, however, compelling other large employers to integrate new technologies related to e-commerce ordering and delivery into their business strategies, while smaller firms continue to take a more cautious approach.

The market dominance of a firm like Amazon means that it has an outsize influence on the competitive landscape of an industry, including on employer objectives regarding technology. Firms with the means to invest are focused on technologies that can promote speed in processing, packaging, delivery, and cost-reduction at every step of the way. Large retailers are also concerned with strategies to increase their own market share and to develop new revenue streams. For some smaller retailers, however, growing consumer expectations about low-cost, rapid online ordering and delivery options are reducing their ability to attract customers, further constricting resources available to adopt new technologies.

In industries where ownership consolidation is less extreme, employers still face similar pressures to reduce wages, speed up work, and micromanage their workers, but a wider range of strategies related to technology adoption can exist. In the grocery and trucking industries, for instance, there are major employers with unionized employees. At unionized firms, workers can exercise their collective market power to influence the effects of new technologies on their jobs, as in the examples discussed above.

In health care, the government is the dominant market actor. Although most people in the U.S. have private insurance through their employers, the U.S. government funds Medicare and Medicaid, making it the largest single buyer of insurance in the U.S. As such, it has significant leverage and buying power in the industry. Thus, when President Obama signed the 21st Century Cures Act, which required home care agencies that provide personal care services to have EVV technologies or risk losing their Medicaid claims, "the federal government essentially 'picked a winner,'" ensuring that this type of technology would proliferate across the market for home care.

Industry trends

Ownership consolidation is not the only industry trend creating distinct patterns of technological change. As another important example, changes in consumer preferences also have a profound effect on patterns of technology adoption in each industry. The increase in e-commerce has reoriented employer objectives across several of the industries we studied, including retail, grocery, warehouses, and trucking. Developing effective platforms for online shopping is of course a priority for many employers, as is deploying a wide range of technologies to facilitate rapid order assembly and delivery. Similarly, growing consumer demand for prepared or semi-prepared food rather than groceries has put pressure on traditional grocery stores to develop more options in this area. One result is that some grocery stores are partnering with subscription-based meal kit companies, which typically rely on warehouse-like facilities where workers' wages and job quality are comparatively low.[58]

In the warehouse industry, there are important differences between production processes for shipping in e-commerce facilities and traditional warehouses. Processing in traditional facilities involving bulk transfer of products is far less labor-intensive and time-sensitive compared to processing in e-commerce facilities. As a result, traditional warehouse employers may be less interested in the kind of intensive employee surveillance pioneered by Amazon, and more interested in advances in warehouse management systems and machinery to automate movement of goods around a facility.

Another industry trend affecting technological adoption is outsourcing. We've already discussed outsourcing in the context of trucking, grocery, and food delivery, and the implications of these changes for workers, in particular independent contractors, who lack basic employee rights and protections on the job. There has also been an increase in recent years in firm-to-firm outsourcing in some sectors, such as retailers outsourcing warehouse services to third-party logistics companies ("3PLs"). Competition in the 3PL market is extremely price-sensitive, and contracts are often short-term, which may blunt employers' incentives to invest in specialized technology.

Broader societal and market trends

The COVID-19 pandemic, long-term demographic changes, and shifts in economic conditions and labor markets all affect patterns of technological change across multiple industries, but the consequences of these trends are not uniform across or even within industries. In particular, the pandemic appears to be speeding up technology adoption in some cases but slowing it down in others. For example, grocery employers have rapidly shifted to online ordering and health care providers to telehealth options since 2020; these are both technologies that had made limited inroads prior to the pandemic. On the other hand, financial constraints in stores and restaurants caused by declines in in-store shopping and eating have limited some employers' ability to experiment or invest in any new technologies.

COVID-19 has also reshaped employers' objectives and strategies related to technology. In industries where workers have been required to work onsite, such as health care, grocery stores, and warehouses, technologies that could help reduce disease transmission for customers or workers became a priority for some employers.[62] These included new forms of data gathering on workers' health to prevent and track outbreaks, and technologies that reduce the need for on-site workers or reduce their physical proximity to one another, such as semi-autonomous service robots and cashierless checkout.

Societal and demographic trends affect industries' uptake of technology as well. Women's long-term increases in labor force participation has induced growing demand for prepared food along with online ordering of groceries and takeout. In the U.S., people are also living longer on average, which creates an interest among health care providers in technologies that can respond to the increasing demand for long-term care. Additionally, an aging workforce has led to a retirement bubble in certain industries that may exacerbate ongoing labor shortages, with a prime example being trucking.

Broader trends may work in favor of or against workers in terms of technology adoption and its consequences. Labor shortages are benefiting some workers in sectors like trucking and warehousing by prompting long-overdue wage increases, but they may also accentuate employers' interest in technologies that reduce employers' reliance on workers in the long-run. As discussed above, the pandemic has likely accelerated some employers' interest in labor-displacing technologies and employee surveillance technologies, but it has also reduced some employers' ability to invest in such technologies, potentially slowing the pace of change in certain industries and market segments.

Conclusions

Recent technological advances such as big data, robotics, and artificial intelligence have expanded our technological capabilities exponentially, and are poised to accelerate the pace of change in many industries. In some cases, employers will deploy new technologies in ways that reduce the number of jobs available. In other cases, they will use new technologies in ways that expand the numbers of existing jobs or create entirely new types of positions. Employers also will continue to use new technologies to reorganize production, reshuffle tasks, and change work processes. Across all the industries our researchers studied, we found that new technologies pose a significant threat to workers' wages and job quality, and equity for women, people of color, and other disadvantaged workers. Many employers are using-or exploring the use of-technologies that facilitate monitoring and control over workers' actions, speed up the pace of work, and generally devalue workers and their contributions.

The COVID-19 pandemic has injected additional uncertainty into forecasting the future of work; it serves as an important reminder that the form new technologies take and the consequences they have are not preordained. What the jobs of tomorrow will look like depends on the choices we make now to shape the course of tech development and adoption, and on the mitigable ground rules for the economy overall and for the industries in which these decisions are situated. Our studies illustrate how employers' choices about technological change occur within the context of specific industries and markets, and the regulations and institutions that guide and govern how firms function.

The same forces that have constrained workers' ability to improve their wages and working conditions are currently on course to wield disproportionate influence over the types of technologies that are developed and the ways they are deployed in each industry. Through our industry studies we have highlighted the importance of regulations to allow workers to help shape the process of technological change, and for employers to be motivated to make choices that prioritize quality-based over cost-based competition. Industry regulation and labor market institutions matter in guiding employer options and choices around technology adoption, and the resultant outcomes for workers.

In industries with some presence of unions-health care, trucking, and grocery-our researchers identified examples of workers influencing the process of new technology adoption to create better outcomes for workers. However, even in these industries, our researchers found that broader societal and economic trends are likely to continue to push employers to use new technologies in ways that increase the challenges workers face on the job. In industries like non-food retail and warehouses, where unions are less common and where large firms are able to set the terms of competition, prospects for workers to experience beneficial effects from technological change are even bleaker.

Absent an intentional shift in our current approaches to technological change, the industries our researchers studied are likely to see employers use new technologies in ways that make job quality worse and widen economic and race and gender inequality in the labor market. So how do we change our course? We know that new technologies can be used in ways that support workers, but how do we get there? The findings of our studies suggest that achieving better outcomes for workers will take a multifaceted approach including industry-specific institutional and regulatory changes, broader policy changes, and workplace-based organizing.

Acknowledgements:

The UC Berkeley Labor Center and Working Partnerships USA commissioned the original research discussed in this piece. Funding for that project was provided by the Ford Foundation, the Open Society Foundations, the SEIU California State Council, the W.K. Kellogg Foundation, the David M. and Abby Joseph Cohen Summer Research Fund, and the Cornell University ILR School "Technology and the Future of Work" theme project. Special thanks to Annette Bernhardt for your leadership throughout the project, and your thoughtful input and review of this report. We'd also like to thank the reviewers for sharing insights and feedback: Chris Benner, Jeffrey Buchanan, Françoise Carré, Beth Gutelius, Sara Hinkley, Ken Jacobs, Lisa Kresge, Adam Seth Litwin, Jenifer MacGillvary, Chris Tilly, and Steve Viscelli. Please address correspondence to the author at jesshf@berkeley.edu. Any errors are the responsibility of the author.

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Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data & Society, 8(2), 20539517211020332. https://doi.org/10.1177/20539517211020332

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[1] There is of course a substantial and growing body of theoretical and empirical literature on technological change and work, including but not limited to the question of automation. For some recent examples see Acemoglu & Restrepo (2020); Agrawal et al. (2019); Hu (2022); Jarrahi et al. (2021); Kellogg et al. (2020); Moradi & Levy (2020); Rogers (2020). For a discussion of methodological approaches to studying technology and work, see Bailey & Barley (2020).

Benner et al. (2020); Carré et al. (2020); Gutelius & Theodore (2019)

Gutelius and Theodore (2019), p. 6

Gutelius & Theodore (2019)

For further discussion on automation and tasks, see: Acemoglu & Restrepo (2019); and Manyika et al. (2017)

For a deeper discussion of the ways employers can use algorithms in the workplace, see chapter 5 of Kresge (2020a)

Gutelius & Theodore (2019)

Gutelius & Theodore (2019)

Gutelius & Theodore (2019)

Benner et al. (2020); Carré et al. (2020)

Data collection on workers often serves another function besides monitoring, task direction and pace-setting: generating data to inform the development of new tools and machines to automate certain types of tasks.

Eubanks & Mateescu (2021)

Workers are also monitored as a by-product of employers' use of video monitoring to track customers as well (e.g., for shoplifting or for tracking customer shopping habits). (Carré et al. 2020)

Gutelius & Theodore (2019)

Bernhardt et al. (2021); Heilweil (2020)

Gutelius & Theodore (2019) p. 61.

Viscelli (2018), p. 7. Levy (2015) notes that certain regulations have increased in trucking: some Department of Transportation rules introduced over the past few decades have actually led to increased electronic monitoring of truckers.

Additionally, although labor is relatively inexpensive, the U.S. tax code favors employer investments in capital over investments in labor in a variety of ways, which may incentivize firms to pursue labor-replacing technologies in the long run (Acemoglu et al. 2020).

[47] Connecticut and Minnesota passed similar bills, and one was proposed in Washington as well. Both chambers of the New York State Legislature have passed the Warehouse Worker Protection Act, though it has yet to be signed by the governor. (An Act Concerning Protection of Warehouse Workers, 2022; Concerning Warehouse Distribution Centers., 2021; HF 2774 Status in the Senate for the 92nd Legislature (2021 - 2022), 2021; Roth, 2022).

[49] However, companies like Uber, Lyft, and Doordash continue attempting to weaken and overturn these laws. In 2020, Uber, Lyft and Doordash spent over $200 million in California to pass Proposition 22, which invalidated the law extending employment rights to many app-based workers. An Alameda County Superior Court Judge ruled the ballot measure unconstitutional in 2021, but it remains in effect during the app companies' appeal. (Conger & Browning 2021; Lyons 2021).

Kresge (2020b). It's important to point out that there are ongoing debates around the extent of unions' legal right to bargain over employers' decision-making regarding technology, versus the effects of their decisions regarding technology. See Ashford & Ayers (1987); Rasband (1989)

[52] Platooning involves electronically linking a series of trucks. In human-human platooning, humans drive each truck, but the lead truck controls the speed and braking in the following truck(s). In human-drone platooning, the lead truck is driven by a human, and subsequent truck(s) are controlled by technology. (Viscelli, 2018).

[54] Howland (2021)

Benner et al., (2020); Carré et al. (2020); Gutelius & Theodore (2019)

[58] Benner et al. (2020) p. 17, 61

Gutelius & Theodore (2019) p. 18

Gutelius & Theodore (2019) p. 49

Benner et al. (2020); Carré et al. (2020); Litwin (2020)

[62] Many other employers seriously neglected worker safety during COVID, for instance by supplying insufficient protective equipment or sick leave accommodations, or by not enforcing masking rules for customers.

Carré et al. (2020); Gutelius & Theodore (2019); Litwin (2020)

Gutelius & Theodore (2019)

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Technological change in five industries: Threats to jobs, wages, and working conditions - UC Berkeley Labor Center

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Understanding how technological changes may unfold in different industries is essential for developing effective solutions to the challenges that workers face. In this report, we synthesize the findings of five industry studies: trucking, warehouses, health care, retail, and food delivery.

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Workforce of the Future: The Government AI Reskilling Imperative

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Workforce of the Future: The Government AI Reskilling Imperative

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AI Index Report 2023 - Artificial Intelligence Index

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The AI Index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. The annual report tracks, collates, distills, and visualizes data relating to artificial intelligence, enabling decision-makers to take meaningful action to advance AI responsibly and ethically with humans in mind.

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Generative AI and the Future of Work in America

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Generative AI and the Future of Work in America

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Generative AI and the future of work in America

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Generative AI has the potential to change the future of work in America. We look at which jobs will be in demand and which ones may be at risk.

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Generative AI and Its Impact on State Government IT Workforces

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Putting AI to Work: Technology and Policy for Enabling the Workforce of the Future

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Putting AI to Work: Technology and Policy for Enabling the Workforce of the Future

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The Future of Public Sector Work: Human-Centered Technology and Policy Strategies

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The Future of Public Sector Work: Human-Centered Technology and Policy Strategies

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EO 24-01 AI Workgroup Meeting 6.26.24 - To Share

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FINAL CHARTER - EO 24-01 Section 9 Work Group

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Interim Guidelines for Purposeful and Responsible Use of Generative AI

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Interim Guidelines for Purposeful and Responsible Use of Generative AI

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Executive Order 24-01 on Generative Artificial Intelligence

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Executive Order 24-01 on Generative Artificial Intelligence

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Technology in the Public Sector and the Future of Government Work

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Technology in the Public Sector and the Future of Government Work

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Executive Summary

More than 20 million people-about 15% of the United States workforce-work for a local, state, or federal government entity. A majority of these work in local government (e.g., schools, police and fire departments, county social service agencies), about a third in state government (e.g., universities, tax bureaus, state hospitals), and the remainder in federal government (e.g., post offices, national parks). Millions more work for private employers who receive most or all of their funding from public contracts or grants.

With the exception of the military, government has been generally slower to adopt technology than the private sector. Reasons for this include lack of funding, higher public scrutiny, complex contracting processes, lack of internal IT capacity, and agency fragmentation. The slow pace of technology adoption in some cases has led to both costly and cumbersome service provision; the vision of digital government outlined by federal policymakers in the 1990s has yet to be realized. Greater use of technology by governments holds a lot of promise for both workers and the public: it can remove some of the time-consuming and glitchy processes that frustrate everyone, allow workers to focus on the complexity inherent in providing public services, make government more accessible to more people, and get assistance more quickly into the hands of people who need it.

But there are reasons to be attentive to how technologies are rolled out, especially as the recent jump in technology funding opens up the floodgates of consultants and contractors pitching their products. Technology cannot be used to paper over the lack of investment in the public sector that has characterized the past two decades. In fact, technology presents the greatest risk when it's simply layered on top of already overwhelmed workers and processes, because there is no capacity built in for evaluation and recalibration to ensure that the technology is working as intended. Within the public sector there is enormous variation in size, resource capacity, mission, and political and social context, all of which affect whether and how technology is implemented. But nearly all public sector employers have spent the past decades watching revenues fail to keep up with the costs of providing government services. Since 2008, public sector employment has been stagnant or declining, while private sector employment has grown by 12% and the U.S. population-a measure of demand for government services-by nearly 7%.

Some technologies also present inherent risks, such as those intended to replace or supplement human decision-making. Research suggests that people are reluctant to make different decisions than those suggested by analytics designed to supplement human decision-making, sometimes leading to worse outcomes than those the technology was intended to remediate. There has been considerable evidence that advanced technologies can replicate or even exacerbate racial and ethnic biases. Governments should be deliberate and cautious as they adopt such technologies. Involving workers in the scoping, design, implementation, and evaluation of advanced technologies in particular can help safeguard public trust. Technology as a cost-saving measure must be implemented within a framework that recognizes the role public workers play in assessing whether systems are serving the people the programs are intended to serve.

How governments use technology

The public sector covers an enormous set of occupations and activities, and technology plays many different roles within that landscape. This report sorts technologies into five overlapping categories:

Manual task automation: technologies that replace physical processes or tasks performed by a person. This includes such technology as document scanners, mail sorting machines, digital printers, "smart" parking meters, transcription software, driverless transit, robotic vacuums, and automated toll collectors.

Process automation: technologies that process information or automate interactions between workers and clients. This includes e-Government processes like online payments and benefit applications, as well as more complex automation such as customer service chatbots and "robotic process automation" (RPA). More complex process automation technologies may use artificial intelligence to "learn" from interactions, rather than relying entirely on human programming.

Automated decision-making systems: the use of complex computer programming to replace or augment human decision-making. This group of technologies includes artificial intelligence, machine learning, and predictive analytics. By processing large amounts of data and using human-programmed algorithms or more complex artificial intelligence, ADM systems generate decisions and assessments.

Integrated data systems: integrated data systems and networked cloud storage allow vast amounts of public data to support automation and automated decision-making technologies, as well as provide public access to information about government activities and enable more robust performance evaluation and management.

Electronic monitoring: technologies such as cameras and drones may be used to enforce laws or regulations and feed information into other government processes. Monitoring technologies built into software used by workers can also enable new forms of performance evaluation.

Key findings on government technology use:

There are many examples of innovative agencies using cutting-edge tech, but it is generally true that governments have been slower to modernize than the private sector. Financial constraints, reliance on external contractors, limited in-house IT expertise, and the challenges of providing equitable services to millions of people are just some of the most significant constraints faced by public technology adopters.

Process automation is widely used by governments, and much of it has made public services easier for people to access and while freeing up workers from often overwhelming amounts of paperwork. But there is still much to negotiate about how to serve clients with limited access to electronic services, and how to integrate technology with the complex and idiosyncratic knowledge that humans (caseworkers, counselors, parole officers) use every day to provide effective and personalized support.

Advanced technologies-algorithms, artificial intelligence, robotic process automation-have begun to change some public jobs significantly, either augmenting or replacing some human decision-making, especially in areas of public safety and welfare services. Community and civil liberties advocates are concerned about government's increasing reliance on technologies for complex decision-making and monitoring.

Drivers of technology adoption

In the public sector there are many different (and often overlapping) motivations for adopting new technologies, which in turn shape the design of the tech, the goals of implementation, and how these systems are evaluated. Nothing about the process of change is inevitable; it is highly dynamic and contingent.

Given these complex structures, this report looks at four driving forces underlying the expansion of technology in the public sector:

Efficiency and cost reduction. In many areas of government, per capita revenue has declined over time as a result of tax-cutting politics, forcing governments to figure out how to provide services in an increasingly constrained environment. Promises that technology can increase efficiency and reduce labor and other costs carry a lot of weight in this context. On other hand, prolonged austerity has constrained funding for technology and other infrastructure.

Performance. Technology is framed as a core element of promises to make government serve people better and reinstill confidence in government. Technology can potentially improve many aspects of government service: speed, reliability, accuracy, convenience, and even program outcomes (although digitization or automation can also lead to deterioration of service quality).

Transparency and accountability. Technological advances in secure data storage, data sharing, data analytics, and data visualization have the potential to enhance government transparency and accountability. Increased data accessibility allows citizens to understand how resources are being used and whether programs are effective. Transparency is a necessary step toward accountability; advocates for robust data sharing hope it will enable the public to hold their governments accountable to specific objectives and values.

Crises. Crises can offer important pivotal moments for innovation-and in the case of COVID-19, large amounts of funding for new technologies-but they can also leave agencies too overwhelmed to incorporate technology strategically. The COVID-19 pandemic dramatically accelerated the adoption of technology in the public sector, as agencies had to figure out how to quickly pivot to offering services while complying with public health orders. As the pandemic unfolded and unprecedented numbers of people needed government services, the public sector's outdated technological infrastructure was exposed along with other areas of underinvestment.

Key findings on technology drivers:

Technology use has allowed many government agencies to restructure cumbersome processes, becoming more user-friendly and increasing productivity by allowing workers to focus on more complex tasks.

The fiscal and workload pressures faced by governments have led many agencies to see technology as a way to bridge the deficit of resources needed to adequately perform their core functions. Technology use can normalize the inadequacy of public staffing rather than resolving it. Chatbots might allow clients to interact with a system 24 hours a day instead of waiting in line in a benefits office and never getting to the front, but if the chatbot is ultimately unable to provide entitled benefits, technology has provided only the illusion of better service.

The COVID-19 pandemic sparked a wave of technology adoption across the public sector, in some cases hastening already planned transformations. Areas like education, where there has typically been significant skepticism about the role of technology, saw an explosion of "edtech" vendors eager to capitalize on schools' experience with using technology for remote learning. The apparent permanency of hybrid and remote work is likely to continue to drive increased automation, monitoring of workers, and reliance on cloud-based data systems.

The public sector must contend with complicated policy, social, ethical, and legal contexts that don't similarly constrain private sector actors. Public sector technology projects are accountable to a more diverse set of stakeholders (often with diverse needs) than private employers. Values of transparency and fairness make adopting new technologies much more complex for the public sector, which must ensure that its services are accessible to everyone and accountable to a broad set of public values.

The public sector has struggled to attract and sustain internal IT expertise, and has often relied on outsourcing many IT functions; this has led to a significant reliance on "govtech" companies and consultants. Building internal IT capacity could help address some of the cost overruns and poor outcomes associated with large technology projects.

Impacts on work and workers

The extent to which technology will displace workers or fundamentally change workplace dynamics is uncertain and will vary across the public sector. There are many logistical, ethical, legal, institutional, and social dynamics that affect the trajectory of technology adoption. The growing adoption of complex technologies is likely to restructure work processes as well as to reshape the interactions between workers and the public. Whether these changes ultimately benefit or harm workers will depend significantly on how this restructuring is managed.

This report looks at four categories of impacts on public sector workers:

Employment impacts: when technology is introduced into a workplace, tasks are transformed and redistributed, possibly reducing the need for some occupations and increasing demand for other types of work. It is hard to attribute job fluctuations to technology directly-especially given the cyclical nature of government funding-but this report discusses occupations where automation has likely contributed to declining employment, as well as growing occupations that require more technical skills to oversee and manage computerized processes, including providing direct IT services.

Job complexity: Working with new technologies may require new skills, which aren't always accompanied by training or the time to adapt. Automating technologies may take over the more mundane aspects of work, making jobs more complex and rewarding for workers. But more advanced technologies-such as automated decision-making systems-may have the opposite effect, taking over complex thinking tasks and leaving workers to simply verify outcomes.

Managerial control: incorporating new technologies can lead to work intensification and stress for workers if the tech does not produce the expected efficiency or performance improvements, leaving workers to make up the difference. When tech is adopted without sufficient understanding of how work is actually performed, service quality can suffer. New technologies can also permit additional worker surveillance; chatbot technology can include real-time feedback to workers on their tone and speed during customer interactions, information that feeds into a workers' performance evaluation.

Outsourcing: Bringing in new technologies often involves an increased role for private contractors, with the outsourcing of both the development and implementation of new tech as well as increasing reliance on private entities to perform even highly sensitive public functions. Technologies like cloud-based storage and virtual call centers can facilitate the outsourcing of jobs to private contractors by enabling work to be done from anywhere and shifting tasks out of established job descriptions.

Key findings on worker impacts:

Technologies have taken over some of the tasks performed by government workers, predominantly in areas involving basic paperwork-processing and financial transactions. Occupations like clerks and secretaries have been declining for several years and are projected to continue to decline, likely in part because of technological changes. The growing automation of government processes and adoption of more complex technologies has likely contributed to the increase in higher-level business and financial occupations and computer-related occupations.

The growth of complex technologies has begun to restructure work in significant ways, raising fundamental questions about how technology changes responsibility for decision-making and who is responsible for overseeing and fixing the inevitable malfunctions, mistakes, and negative impacts of digitized processes. Workers often feel stressed and uncertain as their jobs are transformed. They value the improvements technology can bring, but they also see technology projects rolled out without a clear plan for training and worker involvement, without clear expectations of workers, and without internal IT capacity adequate to managing the impacts of technology on the lives of clients.

Despite the relatively high share of public sector workers represented by a union, technological solutions are still frequently developed without involving workers. Governments are just beginning to put policies and regulations in place to manage the impacts of technology on citizens, but there are few examples of such policies addressing the impacts on workers.

People who go into public service often have aspirations to improve people's lives, and are concerned about how technology can deepen existing inequities, make it harder for people to access critical services, and jeopardize the trust between citizens and their government. The possibilities for technology to greatly improve public services are significant, and recent public investments in infrastructure and internal technology capacity development present an important opportunity. A high road approach to this rapidly expanding use of technology will require policies and regulations that bring transparency, accountability, evaluation, and worker and client voices into the process of designing and implementing technology.

Read the full report.

Suggested citation: Hinkley, Sara. 2022. Technology in the Public Sector and the Future of Government Work. Berkeley: UC Berkeley Labor Center. https://laborcenter.berkeley.edu/technology-in-the-public-sector-and-the-future-of-government-work/

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Technology in the public sector and the future of government work - UC Berkeley Labor Center

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This report explores how governments use technology, what drives technology adoption, and how technologies affect public sector workers and the delivery of public services.

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AI Applications and Prompt Engineering 

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AI Applications and Prompt Engineering 

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edX has an introductory course on prompt engineering that goes beyond the basics. Start by learning the basics and end knowing how to create your own applications.

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Big Data, Artificial Intelligence, and Ethics

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Big Data, Artificial Intelligence, and Ethics

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In this 4-module course, the University of California - Davis covers big data and introduces IBM's Watson. Start by learning about big data opportunities and end knowing the limitations of AI.

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Big Data, Artificial Intelligence, and Ethics

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Offered by University of California, Davis. This course gives you context and first-hand experience with the two major catalyzers of the ... Enroll for free.

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Google Cloud in collab with DeepLearning is offering this brand-new course taught by Erwin Huizenga. Go through the LLMOps pipeline of pre-processing training data and adapt a supervised tuning pipeline to train and deploy a custom LLM.

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In this course, you'll go through the LLMOps pipeline of pre-processing training data for supervised instruction tuning, and adapt a supervised tuning pipeline to train and deploy a custom LLM. This is useful in creating an LLM workflow for your specific application. For example, creating a question-answer chatbot tailored to answer Python coding questions, which you'll do in this course.

Through the course, you'll go through key steps of creating the LLMOps pipeline:

Retrieve and transform training data for supervised fine-tuning of an LLM.

Version your data and tuned models to track your tuning experiments.

Configure an open-source supervised tuning pipeline and then execute that pipeline to train and then deploy a tuned LLM.

Output and study safety scores to responsibly monitor and filter your LLM application's behavior.

Try out the tuned and deployed LLM yourself in the classroom!

Tools you'll practice with include BigQuery data warehouse, the open-source Kubeflow Pipelines, and Google Cloud.

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What you'll learn in this course

In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. Using the OpenAI API, you'll be able to quickly build capabilities that learn to innovate and create value in ways that were cost-prohibitive, highly technical, or simply impossible before now. This short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) will describe how LLMs work, provide best practices for prompt engineering, and show how LLM APIs can be used in applications for a variety of tasks, including:

Summarizing (e.g., summarizing user reviews for brevity)

Inferring (e.g., sentiment classification, topic extraction)

Transforming text (e.g., translation, spelling & grammar correction)

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In addition, you'll learn two key principles for writing effective prompts, how to systematically engineer good prompts, and also learn to build a custom chatbot. All concepts are illustrated with numerous examples, which you can play with directly in our Jupyter notebook environment to get hands-on experience with prompt engineering

In partnership with OpenAI

We are excited to collaborate with OpenAI in offering this course, designed to help developers effectively utilize LLMs. This course reflects the latest understanding of best practices for using prompts for the latest LLM models.

Who should join?

ChatGPT Prompt Engineering for Developers is beginner-friendly. Only a basic understanding of Python is needed. But it is also suitable for advanced machine learning engineers wanting to approach the cutting-edge of prompt engineering and use LLMs.

Generative AI offers many opportunities for AI engineers to build, in minutes or hours, powerful applications that previously would have taken days or weeks. I'm excited about sharing these best practices to enable many more people to take advantage of these revolutionary new capabilities.

- Andrew Ng

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ChatGPT Prompt Engineering for Developers

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What you'll learn in this course In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. Using the OpenAI API, you'll...

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Learn to use machine learning in Python in this introductory course on artificial intelligence.

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Google offers 5 different courses to learn generative AI from the ground up. Start with an Introduction to AI and finish having a solid understanding of AI as a whole.

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Beginner: Introduction to Generative AI | Google Skills

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Learn and earn with Google Skills, a platform that provides free training and certifications for Google Cloud partners and beginners. Explore now.