Tech Worker Organizing for Power and Accountability
Abstract
In recent years, there has been a growing interest in the field of “AI Ethics” and related areas. This field is purposefully broad, allowing for the intersection of numerous subfields and disciplines. However, a lot of work in this area thus far has centered computational methods, leading to a narrow lens where technical tools are framed as solutions for broader sociotechnical problems. In this work, we discuss a less-explored mode of what it can mean to “do” AI Ethics: tech worker collective action. Through collective action, the employees of powerful tech companies can act as a countervailing force against strong corporate impulses to grow or make a profit to the detriment of other values. In this work, we ground these efforts in existing scholarship of social movements and labor organizing. We characterize 150 documented collective actions, and explore several case studies of successful campaigns. Looking forward, we also identify under-explored types of actions, and provide conceptual frameworks and inspiration for how to utilize worker organizing as an effective lever for change.
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Internal auditors face the threat of firings, social dismissal or professional demotion.
“who face the threat of firings, social dismissal or professional demotion [73, 110]”
From Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2025) · cites this paper as [110]
Cites
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · see citing works
Model Cards for Model Reporting
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Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372873 · Cited by 1,372 · cited 2× · see citing works
“[56], co-designing checklists for AI fairness with practitioners [43], and a series of questions for self-assessment of whether AI products are respecting human rights [65].”
BackgroundWhy Civil Resistance Works: The Strategic Logic of Nonviolent Conflict
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The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition
Keyes, O. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274357 · Cited by 505 · see citing works
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Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People
Obermeyer, Z., & Mullainathan, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287593 · Cited by 124 · see citing works
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Harms from Increasingly Agentic Algorithmic Systems
Chan, A., Salganik, R., Markelius, A., Pang, C., Rajkumar, N., Krasheninnikov, D., Langosco, L., He, Z., Duan, Y., Carroll, M., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594033 · Cited by 149 · see citing works
“Collective self-governance is about power, which is a core theme in FATE work [Abdalla and Abdalla 2021; Barabas et al. 2020; Birhane et al. 2022; Boag et al. 2022; Kasy and Abebe 2021; Young et al. 2022].”
BackgroundTowards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Ojewale, V., Steed, R., Vecchione, B., Birhane, A., & Raji, I. D. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713301 · Cited by 81 · see citing works
- Authors
- Boag, W., Suresh, H., Lepe, B., & D'Ignazio, C.
- Affiliations
- Massachusetts Institute of Technology, USA
- Venue
- 2022 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2022-06-20
- Pages
- 452-463
- Cited by
- 26
- References
- 7
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