It's about power: What ethical concerns do software engineers have, and what do they (feel they can) do about them?
Abstract
How do software engineers identify and act on their ethical concerns? Past work examines how software practitioners navigate specific ethical principles such as “fairness”, but this narrows the scope of concerns to implementing pre-specified principles. In contrast, we report self-identified ethical concerns of 115 survey respondents and 21 interviewees across five continents and in non-profit, contractor, and non-tech firms. We enumerate their concerns – military, privacy, advertising, surveillance, and the scope of their concerns – from simple bugs to questioning their industry’s entire existence. We illustrate how attempts to resolve concerns are limited by factors such as personal precarity and organizational incentives. We discuss how even relatively powerful software engineers often lacked the power to resolve their ethical concerns. Our results suggest that ethics interventions must expand from helping practitioners merely identify issues to instead helping them build their (collective) power to resolve them, and that tech ethics discussions may consider broadening beyond foci on AI or Big Tech.
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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 [73]
Cites
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Methods of coping with social desirability bias: A review
Nederhof, A. J. (1985) · European Journal of Social Psychology · doi:10.1002/ejsp.2420150303 · Cited by 2,340 · cited 1× · see citing works
“Interviews and surveys collect self-reported experiences, risking social desirability [48], and hindsight biases [28].”
BackgroundPrinciples alone cannot guarantee ethical AI
Mittelstadt, B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0114-4 · Cited by 1,886 · cited 1× · see citing works
“…argue that the convergence around codified principles like “fairness” or “ac-countablity” obscures underlying political and normative disagreements [47], and there is increasing evidence for this: AI practitioners have different values than the general public in AI system design [32], and workers…”
BackgroundThe accuracy, fairness, and limits of predicting recidivism
Dressel, J., & Farid, H. (2018) · Science Advances · doi:10.1126/sciadv.aao5580 · Cited by 1,071 · see citing works
Defining AI in Policy versus Practice
Krafft, P. M., Young, M., Katell, M., Huang, K., & Bugingo, G. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3375627.3375835 · Cited by 129 · see citing works
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Towards 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
- Widder, D. G., Zhen, D., Dabbish, L., & Herbsleb, J.
- Affiliations
- Carnegie Mellon University, USA
- Swarthmore College, USA
- Venue
- 2023 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2023-06-12
- Pages
- 467-479
- Cited by
- 76
- References
- 7
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