27 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
The December 2020 open letter from Google Walkout for Real Change supporting Timnit Gebru after her departure from Google's Ethical AI team, with its list of signatories. An advocacy document written by her supporters.
A nonprofit that works to make the tech industry share its prosperity and answer for economic harms. It focuses on housing and working conditions, and publishes research such as the 2026 California AI Compass.
Why I recommend it: An advocacy group, so its reports argue a position. Its research on contract workers and AI in the workforce is useful for anyone weighing a tech job.
A DAIR Institute project for unions, labor organizations and worker-organizers dealing with AI and automation at work: case studies, primers and a resource library on worker-led oversight of new technology.
From the site: Research and case studies.
Why I recommend it: Free. Written from an explicitly pro-worker position, which it states openly — useful if you want the labor perspective on workplace AI rather than the vendor one.
A free newsletter briefing on AI developments from a pro-human standpoint, covering labor, safety, policy and the campaigns pushing back on automation-first decisions.
Why I recommend it: A steady weekly read if you want the human-impact side of AI news rather than product launches.
Washington, D.C. think tank publishing free research and policy briefs on jobs, apprenticeships, career pathways for non-college workers, trade, AI and the economy — all readable without a subscription.
Why I recommend it: Useful when you want numbers rather than opinions about where jobs and training money are actually going. Their work on apprenticeships and career pathways for people without a degree is the part I send clients most.
The open letter and campaign from employees pressing their employer on climate and data-center energy use.
Why I recommend it: An example of workers using an open letter as leverage — worth reading for the wording alone.
An investigation into healthcare workers striking over conditions and automation in mental-health care.
Why I recommend it: A reminder that automation debates land hardest on care work.
A running database of strikes, petitions and campaigns by workers responding to AI in their workplaces.
Why I recommend it: Good evidence base if you are writing or speaking about AI and jobs.
Worker-led research and mental-health resources by and for the data annotators who label the material AI systems learn from.
Why I recommend it: Written by the workers themselves, not about them.
An association organizing the data-labeling workforce behind AI training data around pay, conditions and recognition.
Why I recommend it: A concrete answer to "who actually built this model" — and what they were paid.
An advocacy organization working on the human cost of mineral extraction that supplies the global electronics and AI supply chain.
Why I recommend it: The hardware behind AI starts in mines — this is the part of the story most coverage skips.
A volunteer network of technology workers organizing around labor conditions, ethics and accountability inside the industry.
Why I recommend it: If you work in tech and want to push from the inside, start with their local chapters.
A directory of worker-owned technology cooperatives you can hire instead of a conventional agency.
Why I recommend it: Practical if you or a client need tech work done and want a different ownership model.
Partnership on AI's open library of guidance, frameworks, and case studies on responsible AI: synthetic media, labor and the economy, AI safety, fairness, and inclusive AI development.
Why I recommend it: When you need a credible source instead of a hot take, cite these. The labor and economy work is the most useful set for career conversations about automation.
A Liberty Street Economics post from the New York Fed arguing that, so far, AI adoption is being used to change how work is done rather than to reduce headcount, based on regional business survey data.
Why I recommend it: A useful counterweight to AI job-loss headlines; helpful for understanding how employers are actually deploying the technology right now.
Economic policy organization researching corporate power, prices, and worker outcomes.
Why I recommend it: Their work on corporate power explains a lot about the labor market job seekers are facing.
Cory Doctorow essay on AI hype, market incentives, and who bears the cost of the buildout.
Why I recommend it: Doctorow is a useful counterweight to any week where the AI news feels inevitable.
A downloadable report from employee-rights firm Outten & Golden on trust in the workplace, covering surveillance, transparency, and worker protections.
Why I recommend it: Written by lawyers who represent employees, not employers. Worth reading before you sign anything that mentions monitoring.
Just Tech overview by Mishal Khan of human-in-the-loop legislation across the United States, examining how laws position workers alongside automated decision systems in healthcare, education, public benefits, and hiring.
Why I recommend it: If your job now includes reviewing an algorithm's output, this explains the rules being written around you and where they fall short.
Introduction by Kelly Joyce and Taylor M. Cruz to a Socius special collection framing AI as a sociotechnical system, with research on AI in health, work and labor, methods, and policy.
Why I recommend it: A clear entry point if you want the research vocabulary for what you already sense about AI at work.
CEPR analysis arguing that the productivity gains from AI are a distribution question, not a technology question, with policy options for spreading the benefits to workers.
Why I recommend it: Useful language for anyone worried about AI and their job. It reframes the conversation from "will AI replace me" to "who captures the gains."
Nonprofit innovation lab that funds and tests worker-driven ideas to build power and improve job quality for working people.
Why I recommend it: Details: watch their funded pilots and calls for applications if you are building something that improves conditions for workers.
Founder, Distributed AI Research Institute (DAIR). AI researcher and prominent voice on AI ethics, bias, and the risks of concentrated corporate control over AI development.
In plain terms: This LinkedIn profile belongs to Dr. Timnit Gebru, an artificial intelligence researcher and founder of the Distributed AI Research Institute. You can follow her page to read updates and commentary on technology ethics, bias, and research.
Why I recommend it: Pairs well with the DAIR entry in the library — independent research, not corporate PR.
Independent nonprofit researching the social implications of data-centric and automated technologies, informing policy and public understanding.
In plain terms: This nonprofit research institute studies how artificial intelligence, automation, and data technologies affect work and society. You can read free reports, guides, and articles or attend public events to understand how emerging technology impacts labor and the economy.
Why I recommend it: Excellent on automated management and surveillance at work.
NYU-based research institute examining the social and political implications of AI, publishing influential annual reports on AI's societal effects.
In plain terms: This research institute analyzes the social, economic, and workplace impacts of artificial intelligence. You can read free reports, policy toolkits, and expert analyses to better understand how AI affects society and the economy.
Why I recommend it: Their reports connect AI directly to jobs and worker power.
Brookings fellow writing on AI, workers, and the future of good jobs.
In plain terms: This newsletter features writing from a Brookings fellow about artificial intelligence, workers, and the future of jobs. You can read it to stay informed about how technology impacts the modern workplace.
From the site: Click to read Molly Kinder on Substack. Launched 15 days ago.
Why I recommend it: Her worker-first framing is exactly how to talk about AI in a job interview.
Ongoing research archive on how AI and automation affect wages, workers, and economic power in the U.S.
In plain terms: This research archive provides articles and reports on how artificial intelligence affects the workforce. You can explore these studies to learn how new technologies and automation impact jobs, wages, and worker protections.
From the site: Content archives for the Washington Center for Equitable Growth’s work on AI, tech, & the economy.
Why I recommend it: Use this when you need real numbers on AI and jobs for a proposal or interview.
