Fairness and Abstraction in Sociotechnical Systems
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A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science---such as abstraction and modular design---are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones.
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Current algorithmic fairness methodologies fail to adequately account for the socially constructed nature of race, instead adopting a conceptualization of race as a fixed attribute.
“Current methodologies fail to adequately account for the socially constructed nature of race, instead adopting a conceptualization of race as a fixed attribute.”
From Towards a critical race methodology in algorithmic fairness (2020) · cites this paper as [108]
Sections 2.1 (The Framing Trap) and 2.3 (The Formalism Trap) criticize fair-ML methods for abstracting away social context and reducing complex concepts of fairness and discrimination to mathematical definitions. The paper does not specifically say that these methods treat race as a fixed attribute or fail to account for race’s socially constructed nature. (LLM, conf=0.84)
Experimental — accuracy not yet measuredBias is a property of the whole sociotechnical system, and deploying an AI hiring tool without accounting for that context is a governance gap, not merely a technical oversight.
“Bias is a property of the whole sociotechnical system, and deploying a tool without accounting for that context is a governance gap, not merely a technical oversight.”
From Rejected by Code: Algorithmic Bias in AI-Driven Recruitment and the Case for HR-IT Governance Frameworks (2026) · cites this paper as [13]
The Introduction treats fairness as a property of social and legal systems, not of technical tools alone. Section 3 says an automated résumé screen must be understood alongside the behavior of hiring managers and candidates, supporting the need to account for context. The paper does not specifically characterize deploying such a tool without that context as a governance gap. (LLM, conf=0.78)
Experimental — accuracy not yet measuredSociotechnical accounts establish that governance must be organizational without naming the functions responsible.
“Sociotechnical accounts establish that governance must be organizational without naming the functions responsible [13].”
From Rejected by Code: Algorithmic Bias in AI-Driven Recruitment and the Case for HR-IT Governance Frameworks (2026) · cites this paper as [13]
The Introduction and “Adopting a heterogeneous engineering approach” argue that fair-ML work must account for institutions, organizational contexts, and human actors. They do not establish a requirement that governance itself be organizational. The paper also names roles such as designers, hiring managers, and judges, rather than leaving responsible functions wholly unspecified. (LLM, conf=0.80)
Experimental — accuracy not yet measuredAI recruitment tools encode and amplify historical bias through proxy variables rather than explicit prejudice, a mechanism established in the literature and, in the disability context, now recognized by a federal court as a plausible basis for liability.
“AI recruitment tools encode and amplify historical bias through proxy variables rather than explicit prejudice, a mechanism established in the literature and, in the disability context, now recognized by a federal court as a plausible basis for liability.”
From Rejected by Code: Algorithmic Bias in AI-Driven Recruitment and the Case for HR-IT Governance Frameworks (2026) · cites this paper as [13]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredMere technical approaches are superficial patches to address bias in machine learning, and careful approaches to fair machine learning should take social and legal context into account.
“It is now acknowledged that mere technical approaches are superficial patches to address bias in machine learning ([63]), and that careful approaches to fair machine learning should take social and legal context into account.”
From Fairness and explainability in automatic decision-making systems. A challenge for computer science and law (2023) · cites this paper as [63]
Taking metrics measured in isolation risks recapitulating the abstraction error that fairness and justice cannot be treated as meaningful for technology separate from a social context.
“Taking metrics measured in isolation risks recapitulating the abstraction error that [69] point out,”
From Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020) · cites this paper as [69]
Traditional metrics for AI like loss may conceal fairness concerns, social impact risks or abstraction errors.
“Traditional metrics for artificial intelligence like loss may conceal fairness concerns, social impact risks or abstraction errors [69].”
From Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020) · cites this paper as [69]
This shift aligns with contemporary critiques of solutionist AI ethics, which caution against reducing sociotechnical problems to engineering challenges.
“This shift aligns with contemporary critiques of solutionist AI ethics, which caution against reducing sociotechnical problems to engineering challenges [33].”
From Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge (2026) · cites this paper as [33]
Purely technical de-biasing efforts tend to fail because they abstract the model away from the organizational context that produces the harm.
“Purely technical de-biasing efforts, they argue, tend to fail because they abstract the model away from the organizational context that produces the harm.”
From Rejected by Code: Algorithmic Bias in AI-Driven Recruitment and the Case for HR-IT Governance Frameworks (2026) · cites this paper as [13]
Selbst et al. argue that treating fairness as a property that can be engineered into a model in isolation, abstracted from the social context of its use, is why technical interventions so often fail.
“Selbst et al. [13] make precisely this point: treating fairness as a property that can be engineered into a model in isolation, abstracted from the social context of its use, is why technical interventions so often fail.”
From Rejected by Code: Algorithmic Bias in AI-Driven Recruitment and the Case for HR-IT Governance Frameworks (2026) · cites this paper as [13]
Surveys indicate AEDTs of all varieties are thought to be widespread, but there is no available data about the baseline rate of adoption of AEDTs generally, let alone systems within LL 144's scope.
“Surveys indicates that AEDTs of all varieties are thought to be widespread, but there is no available data about the baseline rate of adoption of AEDTs generally, let alone adoption of systems that fall within the scope of LL 144 [9, 24, 46].”
From Auditing Work: Exploring the New York City algorithmic bias audit regime (2024) · cites this paper as [46]
Section 4 (Takeaways; page unavailable) mentions an algorithm designed to predict good hires only as a hypothetical example. Section 3.4 (Ripple Effect Trap; page unavailable) discusses a risk-assessment questionnaire, not a survey of AEDT adoption. Searches for AEDT adoption rates, employer surveys, and LL 144 found no passage addressing the claimed prevalence or lack of baseline data. (LLM, conf=0.76)
Experimental — accuracy not yet measuredExisting XAI methods often do not match end-users' mental models, making explanations ineffective and sometimes harmful.
“Such explanations often do not match the end-users' mental models [56], making the explanations ineective [2,40,65] and at times even harmful [13,28,30,47,51].”
From Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust (2023) · cites this paper as [47]
Other factors such as end-user expertise, ethical considerations, and effects on well-being and satisfaction are important for human-AI trust.
“Instead, it is also important to consider other factors, such as the end-user's expertise [65], their ethical considerations [27,29,47], and the eects that the recommended actions and outcomes of trying to reach user goals could have on the end-user's well-being and satisfaction [35].”
From Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust (2023) · cites this paper as [47]
Naive application of existing XAI methods may be ineffective for a broader audience of end-users, if not harmful.
“naive application of existing XAI methods may be ineective [2,40,65] to broader audience of end-users, if not harmful [28,47,49,51].”
From Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust (2023) · cites this paper as [47]
Many recent studies have pointed to the need to incorporate sociotechnical context in design of algorithmic systems.
“Many recent studies have pointed to the need to incorporate sociotechnical context in design of algorithmic systems.”
From Toward situated interventions for algorithmic equity: lessons from the field (2020) · cites this paper as [58]
Fairness is often contextual: a seemingly 'fair' model designed for one context might be misleading, inaccurate, or harmful when applied to a different context.
“However, as existing literature makes clear, fairness is often contextual [57,90,91,131,136]: a seemingly”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [136]
These discriminatory issues are primarily a reflection of training set deficiencies, namely the underrepresentation of certain parts of the input space and the lack of awareness, consideration and attention of the people building these systems.
“Many of these works conclude that these issues are primarily a refection of training set defciencies, namely the underrepresentation of certain parts of the input space [15,22] and the lack of awareness, consideration and attention of the people building these systems [97,136,146].”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [136]
It is generally not possible to fully debias an AI system or guarantee its fairness; rather, the aim is to mitigate fairness-related harms as much as possible.
“Accordingly, given the many complexities of fairness and its contextual nature, it is generally not possible to fully debias an AI system or to guarantee its fairness [80,101,122]; rather, the aim is to mitigate fairness-related harms and other unwanted consequences as much as possible [101,136,143].”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [136]
It may be difficult (if not impossible) for AIaaS providers to exhaustively test and validate their services as free from all kinds of bias in all kinds of contexts.
“As we have shown in this paper, it may be difcult (if not impossible) for AIaaS providers to exhaustively test and validate their services as free from all kinds of bias in all kinds of contexts [127,136].”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [136]
Pymetrics's US patent reports that multivariate ANOVA and Hotelling's T-squared tests concluded that for each game none of the tasks showed significant differences by ethnic group and a subset of tasks showed different scores based on age and gender.
“The multivariate ANOVA and Hotelling's T-squared tests concluded that for each game none of the tasks showed significant differences by ethnic group and a subset of the tasks showed different scores based on age and gender.”
From What does it mean to 'solve' the problem of discrimination in hiring?: social, technical and legal perspectives from the UK on automated hiring systems (2020) · cites this paper as [39]
Pymetrics's patent reports that when comparing the final fit-to-role score, multivariate statistical analyses concluded there were no differences (statistical bias) between sample groups by age (p>0.05), gender (p>0.05) and race (p>>0.1).
“When comparing the final score that assesses the suitability of a person for a position, the multivariate statistical analyses concluded that there were no differences, statistical bias, between samples groups by age (p>0.05),gender (p>0.05) and race (p>>0.1).”
From What does it mean to 'solve' the problem of discrimination in hiring?: social, technical and legal perspectives from the UK on automated hiring systems (2020) · cites this paper as [39]
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Cites
Science in Action: How to Follow Scientists and Engineers through Society
Bijker, W. E., & Latour, B. (1988) · Technology and Culture · doi:10.2307/3105094 · Cited by 10,768 · see citing works
(1987) · Cited by 8,955 · see citing works
Callon, M. (1984) · The Sociological Review · doi:10.1111/j.1467-954x.1984.tb00113.x · Cited by 7,763 · see citing works
Reassembling the social: An introduction to actor‐network‐theory
Turnbull, J. (2008) · New Zealand Geographer · doi:10.1111/j.1745-7939.2008.00151_4.x · Cited by 7,634 · see citing works
Pinch, T. J., & Bijker, W. E. (1984) · Social Studies of Science · doi:10.1177/030631284014003004 · Cited by 3,890 · see citing works
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Lebovits, H. (2018) · Public Integrity · doi:10.1080/10999922.2018.1511671 · Cited by 3,318 · see citing works
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · see citing works
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works
Rankings and Reactivity: How Public Measures Recreate Social Worlds
Espeland, W. N., & Sauder, M. (2007) · American Journal of Sociology · doi:10.1086/517897 · Cited by 2,450 · see citing works
Barley, S. R. (1986) · Administrative Science Quarterly · doi:10.2307/2392767 · Cited by 2,315 · see citing works
Suchman, L. A. (1987) · Cited by 2,279 · see citing works
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159 · see citing works
Barley, S. R. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511618925.007 · Cited by 2,119 · see citing works
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827 · see citing works
Self-citation(2018) · Cited by 1,167 · see citing works
The Conduct of Inquiry: Methodology for Behavioral Science.
Nagel, E., & Kaplan, A. (1965) · American Sociological Review · doi:10.2307/2091353 · Cited by 1,155 · see citing works
The Social Shaping of Technology
Hoch, P. K., MacKenzie, D., & Wajcman, J. (1987) · Technology and Culture · doi:10.2307/3105489 · Cited by 1,044 · see citing works
Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.itcs.2017.43 · Cited by 943 · cited 1× · see citing works
“First, there is no way to arbitrate between irreconcilably conflicting definitions using purely mathematical means [14, 42].”
BackgroundThe Intellectual Challenge of CSCW: The Gap Between Social Requirements and Technical Feasibility
Ackerman, M. S. (2000) · Human–Computer Interaction · doi:10.1207/s15327051hci1523_5 · Cited by 892 · see citing works
Dourish, P. (2006) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1124772.1124855 · Cited by 860 · see citing works
Fairness-Aware Classifier with Prejudice Remover Regularizer
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33486-3_3 · Cited by 694 · see citing works
Zafar, M. B., Valera, I., Gomez Rodriguez, M., & Gummadi, K. P. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052660 · Cited by 632 · see citing works
Morozov, E. (2013) · Cited by 591 · see citing works
Classification Situations: Life- chances in the neoliberal era
Fourcade, M., & Healy, K. (2013) · Accounting, Organizations and Society · doi:10.1016/j.aos.2013.11.002 · Cited by 544 · see citing works
Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse
Hoffmann, A. L. (2019) · Information, Communication & Society · doi:10.1080/1369118x.2019.1573912 · Cited by 535 · see citing works
Meaningful information and the right to explanation
Selbst, A. D., & Powles, J. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx022 · Cited by 516 · see citing works
Self-citationReassembling the Social – An Introduction to Actor Network Theory
Lissandrello, E. (2008) · Comparative Sociology · doi:10.1163/156913308x336453 · Cited by 499 · see citing works
The Id, the Ego, and Equal Protection: Reckoning with Unconscious Racism
Lawrence, C. R. (1987) · Stanford Law Review · doi:10.2307/1228797 · Cited by 499 · see citing works
Kline, R. R., & Pinch, T. (1996) · Technology and Culture · doi:10.2307/3107097 · Cited by 456 · see citing works
Classifying without discriminating
Kamiran, F., & Calders, T. (2009) · 2009 2nd International Conference on Computer, Control and Communication · doi:10.1109/ic4.2009.4909197 · Cited by 409 · see citing works
Algorithms in practice: Comparing web journalism and criminal justice
Christin, A. (2017) · Big Data & Society · doi:10.1177/2053951717718855 · Cited by 379 · see citing works
A Reductions Approach to Fair ClassificationPreprint
Agarwal, A., Beygelzimer, A., Dudı́k, M., Langford, J. C., & Wallach, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.02453 · Cited by 364 · see citing works
When the implication is not to design (technology)
Baumer, E. P. S., & Silberman, M. ‘. (2011) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979275 · Cited by 353 · see citing works
Artificial experts: social knowledge and intelligent machines
(1991) · Choice Reviews Online · doi:10.5860/choice.28-6285 · Cited by 313 · see citing works
Krieger, L. H. (1995) · Stanford Law Review · doi:10.2307/1229191 · Cited by 301 · see citing works
(2007) · Digital Commons at University of Maryland Carey Law (University of Maryland Francis King Carey School of Law) · Cited by 277 · see citing works
Studying those who study us: an anthropologist in the world of artificial intelligence
(2002) · Choice Reviews Online · doi:10.5860/choice.39-4050 · Cited by 269 · see citing works
Data mining for discrimination discovery
Ruggieri, S., Pedreschi, D., & Turini, F. (2010) · TKDD · doi:10.1145/1754428.1754432 · Cited by 250 · see citing works
Computerization and social transformations
Kling, R. (1991) · Science, Technology, & Human Values · doi:10.1177/016224399101600304 · Cited by 249 · see citing works
On the (im)possibility of fairness
Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016) · arXiv · doi:10.48550/arxiv.1609.07236 · Cited by 247 · see citing works
Self-citationLife, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System
Wexler, R. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2920883 · Cited by 200 · see citing works
Assessing Risk Assessment in Action
Stevenson, M. T. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3016088 · Cited by 195 · see citing works
Disparate Impact in Big Data PolicingPreprint
Selbst, A. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2819182 · Cited by 189 · see citing works
Self-citationGrgić-Hlača, N., Zafar, M. B., Gummadi, K. P., & Weller, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11296 · Cited by 184 · see citing works
Toward a Critical Technical Practice: Lessons Learned in Trying to Reform AI
Agre, P. E. (2006) · Cited by 180 · see citing works
The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards
Holland, S., Hosny, A., Newman, S., Joseph, J., & Chmielinski, K. (2018) · arXiv · doi:10.48550/arxiv.1805.03677 · Cited by 175 · see citing works
Algorithmic psychometrics and the scalable subject
Stark, L. (2018) · Social Studies of Science · doi:10.1177/0306312718772094 · Cited by 168 · see citing works
Automation Bias and Errors: Are Crews Better Than Individuals?
Skitka, L. J., Mosier, K. L., Burdick, M., & Rosenblatt, B. (2000) · The International Journal of Aviation Psychology · doi:10.1207/s15327108ijap1001_5 · Cited by 127 · see citing works
Does mitigating ML's impact disparity require treatment disparity?Preprint
Lipton, Z. C., Chouldechova, A., & McAuley, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.07076 · Cited by 125 · see citing works
From BetaMax to Blockbuster: video stores and the invention of movies on video
(2008) · Choice Reviews Online · doi:10.5860/choice.46-0248 · Cited by 109 · see citing works
Hellman, D. S. (2011) · Harvard University Press eBooks · doi:10.2307/j.ctv22d4zp9 · Cited by 104 · see citing works
A Nutritional Label for Rankings
Yang, K., Stoyanovich, J., Asudeh, A., Howe, B., Jagadish, H., & Miklau, G. (2018) · Proceedings of the 2018 International Conference on Management of Data · doi:10.1145/3183713.3193568 · Cited by 103 · see citing works
Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment
Barabas, C., Dinakar, K., Ito, J., Virza, M., & Zittrain, J. (2017) · arXiv · doi:10.48550/arxiv.1712.08238 · Cited by 87 · see citing works
The Right Not to Be Subject to Automated Decisions Based on Profiling
Mendoza, I., & Bygrave, L. (2017) · Springer eBooks · doi:10.1007/978-3-319-64955-9_4 · Cited by 80 · see citing works
Wired Shut, Copyright and the Shape of Digital Culture
Gillespie, T. (2009) · Journal of Information Communication and Ethics in Society · doi:10.1108/14779960910955927 · Cited by 75 · see citing works
Eddie Murphy and the Dangers of Counterfactual Causal Thinking About Detecting Racial DiscriminationPreprint
Kohler‐Hausmann, I. (2025) · doi:10.31219/osf.io/8y9em_v1 · Cited by 73 · see citing works
The Structural Turn and the Limits of Antidiscrimination Law
Bagenstos, S. R. (2006) · California Law Review · doi:10.2307/20439026 · Cited by 56 · see citing works
Disparate Impact in Big Data Policing
Selbst, A. D. (2018) · Digital Commons (University of Georgia School of Law) · Cited by 44 · see citing works
Self-citationEaglin, J. M. (2017) · University of North Carolina School of Law Scholarship Repository (University of North Carolina Hospitals) · Cited by 37 · see citing works
International Practices of Criminal Justice
(2017) · Routledge eBooks · doi:10.4324/9781315144788 · Cited by 29 · see citing works
The Structural Turn and the Limits of Antidiscrimination Law
Bagenstos, S. R. (2006) · California Law Review · doi:10.15779/z38hb00 · Cited by 27 · see citing works
“Fair” Risk Assessments: A Precarious Approach for Criminal Justice Reform
Green, B. (2018) · Cited by 26 · see citing works
Cases and Materials on Employment Discrimination
Zimmer, M. J. (1982) · bepress Legal Repository · Cited by 24 · see citing works
(2018) · Cited by 17 · see citing works
(Invited) 2D Transport and Velocity Modulation in Black Phosphorus Field Effect Transistors
Szkopek, T., Gervais, G., Hemsworth, N., et al. (2016) · ECS Meeting Abstracts · doi:10.1149/ma2016-01/11/749 · Cited by 5 · see citing works
Managing Risk, Managing Race: Racialized Actuarial Science in the United States, 1881-1948
Wiggins, B. A. (2013) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Madeline Akrich. 1992. The de-scription of technological objects. In Shaping Technology/Building Society, Wiebe E Bijker and John Law (Eds.). MIT Press, 205-224.
- Brent Hecht, Lauren Wilcox, Jeffrey P. Bigham, Johannes Schöning, Ehsan Hoque, Jason Ernst, Yonatan Bisk, Luigi De Russis, Lana Yarosh, Bushra Anjum, Danish Contractor, and Cathy Wu. 2018. It's time to do something: Mitigating the negative impacts of computing through a change to the peer review process. https://acm-fca.org/2018/03/29/negativeimpacts/. ACM Future of Computing Blog.
- Wayne R. LaFave. 2017. Criminal law (6th ed.). West Academic Publishing.
- John Law. 1987. Technology and heterogeneous engineering: The case of Portuegese expansion. In The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology, Wiebe E. Bijker, Thomas P. Hughes, and Trevor Pinch (Eds.). MIT Press, 111-34.
Cited by
Sorted by citations
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · see citing works
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · see citing works
“Many of those endeavors are unified under the FAT ML or XAI community (Veale and Binns 2017; Selbst et al. 2018).”
BackgroundRaji, 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 · see citing works
“Traditional metrics for artificial intelligence like loss may conceal fairness concerns, social impact risks or abstraction errors [69].”
BackgroundFerrara, E. (2023) · Sci · doi:10.3390/sci6010003 · Cited by 875 · see citing works
“This survey discusses the different sources of bias in AI and provides examples of each type, including data bias, algorithmic bias, and user bias [17,18].”
BackgroundImproving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300830 · Cited by 777 · see citing works
Kuziemski, M., & Misuraca, G. (2020) · Telecommunications Policy · doi:10.1016/j.telpol.2020.101976 · Cited by 610 · see citing works
Algorithmic Fairness: Choices, Assumptions, and Definitions
Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042720-125902 · Cited by 687 · see citing works
Fairness of artificial intelligence in healthcare: review and recommendations
Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01474-3 · Cited by 607 · see citing works
Methods in predictive techniques for mental health status on social media: a critical review
Chancellor, S., & De Choudhury, M. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0233-7 · Cited by 557 · see citing works
Towards a standard for identifying and managing bias in artificial intelligence
Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022) · doi:10.6028/nist.sp.1270 · Cited by 548 · see citing works
“These conditions cannot be satisfied simultaneously unless under certain constraints [79].”
BackgroundExpanding Explainability: Towards Social Transparency in AI systems
Ehsan, U., Liao, Q. V., Muller, M., Riedl, M. O., & Weisz, J. D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445188 · Cited by 538 · see citing works
Schiff, D. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00270-2 · Cited by 489 · see citing works
“This becomes problematic when AIED systems developed based on certain considerations are implemented in an a different sociotechnical context, a challenge Selbst et al. (2019) refer to as a “portability trap.””
BackgroundMadaio, M. A., Stark, L., Wortman Vaughan, J., & Wallach, H. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376445 · Cited by 436 · see citing works
The ethics of algorithms: key problems and solutions
Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2022) · AI & SOCIETY · doi:10.1007/s00146-021-01154-8 · Cited by 384 · see citing works
When the Machine Meets the Expert: An Ethnography of Developing AI for Hiring
van den Broek, E., Sergeeva, A., & Huysman, M. (2021) · MIS Quarterly · doi:10.25300/misq/2021/16559 · Cited by 330 · see citing works
Rakova, B., Yang, J., Cramer, H., & Chowdhury, R. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449081 · Cited by 329 · see citing works
Towards a critical race methodology in algorithmic fairness
Hanna, A., Denton, R., Smart, A., & Smith-Loud, J. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372826 · Cited by 295 · see citing works
“rent algorithmic fairness frameworks [53, 108].”
Alami, H., Rivard, L., Lehoux, P., Hoffman, S. J., Cadeddu, S. B. M., Savoldelli, M., Samri, M. A., Ag Ahmed, M. A., Fleet, R., & Fortin, J.-P. (2020) · Globalization and Health · doi:10.1186/s12992-020-00584-1 · Cited by 283 · see citing works
Social Biases in NLP Models as Barriers for Persons with Disabilities
Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y., & Denuyl, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.487 · Cited by 274 · see citing works
Participation Is not a Design Fix for Machine Learning
Sloane, M., Moss, E., Awomolo, O., & Forlano, L. (2022) · Equity and Access in Algorithms, Mechanisms, and Optimization · doi:10.1145/3551624.3555285 · Cited by 267 · see citing works
The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making
de Bruijn, H., Warnier, M., & Janssen, M. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2021.101666 · Cited by 264 · see citing works
AI Art and its Impact on Artists
Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., Hanna, A., Flowers, J., & Gebru, T. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604681 · Cited by 265 · see citing works
Hutchinson, B., Smart, A., Hanna, A., Denton, R., Greer, C., Kjartansson, O., Barnes, P., & Mitchell, M. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445918 · Cited by 279 · see citing works
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Shelby, R., Rismani, S., Henne, K., Moon, A., Rostamzadeh, N., Nicholas, P., Yilla-Akbari, N., Gallegos, J., Smart, A., Garcia, E., et al. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604673 · Cited by 279 · see citing works
Lessons from archives: strategies for collecting sociocultural data in machine learning
Jo, E. S., & Gebru, T. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372829 · Cited by 240 · see citing works
On the genealogy of machine learning datasets: A critical history of ImageNet
Denton, E., Hanna, A., Amironesei, R., Smart, A., & Nicole, H. (2021) · Big Data & Society · doi:10.1177/20539517211035955 · Cited by 221 · see citing works
Good proctor or “big brother”? Ethics of online exam supervision technologies
Coghlan, S., Miller, T., & Paterson, J. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00476-1 · Cited by 219 · see citing works
Janssen, M., Hartog, M., Matheus, R., Yi Ding, A., & Kuk, G. (2022) · Social Science Computer Review · doi:10.1177/0894439320980118 · Cited by 192 · see citing works
“Selbst et al. (2019) challenged the notions of fairness and discrimination in the societal context that surrounds ML-based decision making.”
BackgroundMethodAlgorithmic Impact Assessments and Accountability: The Co-construction of Impacts
Metcalf, J., Moss, E., Watkins, E. A., Singh, R., & Elish, M. C. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445935 · Cited by 195 · see citing works
Giovanola, B., & Tiribelli, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01455-6 · Cited by 193 · see citing works
Sánchez-Monedero, J., Dencik, L., & Edwards, L. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372849 · Cited by 196 · see citing works
The ethical use of artificial intelligence in human resource management: a decision-making framework
Bankins, S. (2021) · Ethics and Information Technology · doi:10.1007/s10676-021-09619-6 · Cited by 181 · see citing works
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · arXiv · doi:10.48550/arxiv.2001.00973 · Cited by 179 · see citing works
Jacobs, M., He, J., F. Pradier, M., Lam, B., Ahn, A. C., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445385 · Cited by 177 · see citing works
An empirical characterization of fair machine learning for clinical risk prediction
Pfohl, S. R., Foryciarz, A., & Shah, N. H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103621 · Cited by 164 · see citing works
Algorithmic realism: expanding the boundaries of algorithmic thought
Green, B., & Viljoen, S. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372840 · Cited by 161 · see citing works
Shen, H., DeVos, A., Eslami, M., & Holstein, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479577 · Cited by 171 · see citing works
Toward situated interventions for algorithmic equity: lessons from the field
Katell, M., Young, M., Dailey, D., Herman, B., Guetler, V., Tam, A., Bintz, C., Raz, D., & Krafft, P. M. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372874 · Cited by 153 · see citing works
Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support
Madaio, M., Egede, L., Subramonyam, H., Wortman Vaughan, J., & Wallach, H. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512899 · Cited by 156 · see citing works
Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging · doi:10.1117/1.jmi.10.6.061104 · Cited by 149 · see citing works
Diversity, Equity, and Inclusion in Artificial Intelligence: An Evaluation of Guidelines
Cachat-Rosset, G., & Klarsfeld, A. (2023) · Applied Artificial Intelligence · doi:10.1080/08839514.2023.2176618 · Cited by 138 · see citing works
Considerations for AI fairness for people with disabilities
Trewin, S., Basson, S., Muller, M., Branham, S., Treviranus, J., Gruen, D., Hebert, D., Lyckowski, N., & Manser, E. (2019) · AI Matters · doi:10.1145/3362077.3362086 · Cited by 143 · see citing works
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
“…particular, many have identified the impact of computational modeling and development in social change [Abebe et al. 2020; Jacobs and Wallach 2021; Selbst et al. 2019] and scoped numerous taxonomies of risks, harms, and failures of algorithmic systems [Raji et al. 2022; Shelby et al. 2022;…”
BackgroundRethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
Weinberg, L. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13196 · Cited by 127 · see citing works
Perturbation Sensitivity Analysis to Detect Unintended Model Biases
Prabhakaran, V., Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1578 · Cited by 115 · see citing works
This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology
Mulligan, D. K., Kroll, J. A., Kohli, N., & Wong, R. Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359221 · Cited by 111 · see citing works
Andrus, M., Spitzer, E., Brown, J., & Xiang, A. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445888 · Cited by 111 · see citing works
AI-driven business analytics and decision making
Badmus, O., Rajput, S. A., Arogundade, J. B., & Williams, M. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.24.1.3093 · Cited by 103 · see citing works
DeVos, A., Dhabalia, A., Shen, H., Holstein, K., & Eslami, M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517441 · Cited by 98 · see citing works
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Deng, W. H., Nagireddy, M., Lee, M. S. A., Singh, J., Wu, Z. S., Holstein, K., & Zhu, H. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533113 · Cited by 99 · see citing works
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Suresh, H., & Guttag, J. (2021) · Equity and Access in Algorithms, Mechanisms, and Optimization · doi:10.1145/3465416.3483305 · Cited by 82 · see citing works
ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia
Halfaker, A., & Geiger, R. S. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415219 · Cited by 82 · see citing works
Farrow, R. (2023) · Learning, Media and Technology · doi:10.1080/17439884.2023.2185630 · Cited by 81 · see citing works
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
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
Barocas, S., Guo, A., Kamar, E., Krones, J., Morris, M. R., Vaughan, J. W., Wadsworth, W. D., & Wallach, H. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462610 · Cited by 72 · see citing works
Hard choices in artificial intelligence
Dobbe, R., Krendl Gilbert, T., & Mintz, Y. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103555 · Cited by 65 · see citing works
Equity Beyond Bias in Language Technologies for Education
Mayfield, E., Madaio, M., Prabhumoye, S., Gerritsen, D., McLaughlin, B., Dixon-Román, E., & Black, A. W. (2019) · Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w19-4446 · Cited by 62 · see citing works
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100336 · Cited by 548 · see citing works
Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science
Radford, J., & Joseph, K. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00018 · Cited by 58 · see citing works
Auditing Work: Exploring the New York City algorithmic bias audit regime
Groves, L., Metcalf, J., Kennedy, A., Vecchione, B., & Strait, A. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658959 · Cited by 59 · see citing works
“Surveys indicate that AEDTs of all varieties are thought to be widespread, but there is no available data about the baseline rate of adoption of AEDTs generally, let alone adoption of systems that fall within the scope of LL 144 [12, 28, 52].”
BackgroundBuilding truths in AI: Making predictive algorithms doable in healthcare
Henriksen, A., & Bechmann, A. (2020) · Information, Communication & Society · doi:10.1080/1369118x.2020.1751866 · Cited by 58 · see citing works
Are “Intersectionally Fair” AI Algorithms Really Fair to Women of Color? A Philosophical Analysis
Kong, Y. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533114 · Cited by 58 · see citing works
Insolvent: How to Reorient Computing for Just Sustainability
Becker, C. (2023) · doi:10.7551/mitpress/14668.001.0001 · Cited by 56 · see citing works
Organizational Governance of Emerging Technologies: AI Adoption in Healthcare
Kim, J. Y., Boag, W., Gulamali, F., Hasan, A., Hogg, H. D. J., Lifson, M., Mulligan, D., Patel, M., Raji, I. D., Sehgal, A., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594089 · Cited by 55 · see citing works
“The focus on the health care delivery ecosystem aligns with the growing emphasis on the understanding that a machine learning model is part of a sociotechnical system, and that the other components of the system need to be modeled[19].”
BackgroundBanovic, N., Yang, Z., Ramesh, A., & Liu, A. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579460 · Cited by 55 · see citing works
Yee, K., Tantipongpipat, U., & Mishra, S. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479594 · Cited by 59 · see citing works
Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making
Suresh, H., Lao, N., & Liccardi, I. (2020) · 12th ACM Conference on Web Science · doi:10.1145/3394231.3397922 · Cited by 49 · see citing works
A Framework for Assurance Audits of Algorithmic Systems
Lam, K., Lange, B., Blili-Hamelin, B., Davidovic, J., Brown, S., & Hasan, A. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658957 · Cited by 48 · see citing works
“Scholarship has highlighted that technical measures of risks of harm (such as bias) need to be understood in the sociotechnical context of an algorithmic system [1, 47, 65, 67].”
BackgroundThe Challenge of Value Alignment: From Fairer Algorithms to AI Safety
Gabriel, I., & Ghazavi, V. (2022) · Oxford Handbook of Digital Ethics · doi:10.1093/oxfordhb/9780198857815.013.18 · Cited by 52 · see citing works
Respite for SMEs: A Systematic Review of Socio-Technical Cybersecurity Metrics
van Haastrecht, M., Yigit Ozkan, B., Brinkhuis, M., & Spruit, M. (2021) · Applied Sciences · doi:10.3390/app11156909 · Cited by 45 · see citing works
Challenges and efforts in managing AI trustworthiness risks: a state of knowledge
Polemi, N., Praça, I., Kioskli, K., & Bécue, A. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1381163 · Cited by 46 · see citing works
Moore, J. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00032 · Cited by 42 · see citing works
“Selbst et al. expand on these critiques to note how FAT as a field misses the broader social context and might be better served focusing on process and collaborating deeply with domain experts (Selbst et al., 2019).”
BackgroundMachine Learning Explainability for External Stakeholders
Bhatt, U., Andrus, M., Weller, A., & Xiang, A. (2020) · arXiv · doi:10.48550/arxiv.2007.05408 · Cited by 41 · see citing works
Humans, AI, and Context: Understanding End-Users’ Trust in a Real-World Computer Vision Application
Kim, S. S. Y., Watkins, E. A., Russakovsky, O., Fong, R., & Monroy-Hernández, A. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3593978 · Cited by 37 · see citing works
Sahlgren, O. (2023) · Learning, Media and Technology · doi:10.1080/17439884.2021.1986065 · Cited by 30 · see citing works
Lewicki, K., Lee, M. S. A., Cobbe, J., & Singh, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581463 · Cited by 30 · see citing works
“As we have shown in this paper, it may be difficult (if not impossible) for AIaaS providers to exhaustively test and validate their services as free from all kinds of bias in all kinds of contexts [124, 133].”
BackgroundInfluentialAlgorithmic Bias and Risk Assessments: Lessons from Practice
Hasan, A., Brown, S., Davidovic, J., Lange, B., & Regan, M. (2022) · Digital Society · doi:10.1007/s44206-022-00017-z · Cited by 29 · see citing works
Should Users Trust Advanced AI Assistants? Justified Trust As a Function of Competence and Alignment
Manzini, A., Keeling, G., Marchal, N., McKee, K. R., Rieser, V., & Gabriel, I. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658964 · Cited by 28 · see citing works
“Lastly, we propose a sociotechnical approach [65, 115, 141] to help users understand if and when their trust in the competence and alignment of AI assistants and their developers is justified.”
BackgroundWatkins, E. A., & Chen, J. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3658938 · Cited by 25 · see citing works
Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles
Guillen-Aguinaga, M., Aguinaga-Ontoso, E., Guillen-Aguinaga, L., Guillen-Grima, F., & Aguinaga-Ontoso, I. (2025) · Data · doi:10.3390/data10120201 · Cited by 37 · see citing works
Kirat, T., Tambou, O., Do, V., & Tsoukiàs, A. (2023) · EURO Journal on Decision Processes · doi:10.1016/j.ejdp.2023.100036 · Cited by 24 · see citing works
Igbinovia, M. O., & Mensah Danquah, M. (2025) · Technical Services Quarterly · doi:10.1080/07317131.2025.2512282 · Cited by 22 · see citing works
Toward a science of human–AI teaming for decision making: a complementarity framework
Gonzalez, C., Donahue, K., Goldstein, D. G., Heidari, H., Jalali, M. S., Schelble, B., Singh, A., & Woolley, A. W. (2026) · PNAS Nexus · doi:10.1093/pnasnexus/pgag030 · Cited by 33 · see citing works
Monitoring AI Services for Misuse
Javadi, S. A., Norval, C., Cloete, R., & Singh, J. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462566 · Cited by 21 · see citing works
Fairness Without Demographic Data: A Survey of Approaches
Ashurst, C., & Weller, A. (2023) · Equity and Access in Algorithms, Mechanisms, and Optimization · doi:10.1145/3617694.3623234 · Cited by 21 · see citing works
Chen, Q., & Heydari, B. (2022) · Journal of Mechanical Design · doi:10.1115/1.4055057 · Cited by 12 · see citing works
Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation
Eriksson, M., Purificato, E., Noroozian, A., Vinagre, J., Chaslot, G., Gomez, E., & Fernandez-Llorca, D. (2025) · arXiv · doi:10.48550/arxiv.2502.06559 · Cited by 35 · see citing works
Mbah, G. O. (2024) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.5.1024.3123 · Cited by 11 · see citing works
Malgieri, G. (2021) · Law and Business · doi:10.2478/law-2021-0003 · Cited by 10 · see citing works
ACROCPoLis: A Descriptive Framework for Making Sense of Fairness
Aler Tubella, A., Coelho Mollo, D., Dahlgren Lindström, A., Devinney, H., Dignum, V., Ericson, P., Jonsson, A., Kampik, T., Lenaerts, T., Mendez, J. A., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594059 · Cited by 8 · see citing works
Human-Centered Evaluation and Auditing of Language Models
Liu, Y. L., Deng, W. H., Lam, M. S., Eslami, M., Kim, J., Liao, Q. V., Xu, W., Novikova, J., & Xiao, Z. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3706729 · Cited by 7 · see citing works
Warthon, M. (2024) · Internet Policy Review · doi:10.14763/2024.3.1801 · Cited by 4
Ahmad, H., & Ghafar, N. A. (2026) · AI and Ethics · doi:10.1007/s43681-026-01254-5 · Cited by 3 · see citing works
“This shift aligns with contemporary critiques of solution-ist AI ethics, which caution against reducing sociotechnical problems to engineering challenges [33].”
Perceptions of the Fairness Impacts of Multiplicity in Machine Learning
Meyer, A. P., Kim, Y.-S., D'Antoni, L., & Albarghouthi, A. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713524 · Cited by 1 · see citing works
Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems
Kroll, J. A. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445937 · Cited by 2 · see citing works
Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents
La Malfa, G., Meegahapola, L., Bogucka, E., Zhang, J. M., Luck, M., Black, E., & Quercia, D. (2026) · arXiv · doi:10.48550/arxiv.2608.08601
“Socio-technical approaches take a broader view, framing AI as part of a wider social sys-tem and classifying large-scale risks such as unfair resource distribution or the erosion of accountability (Selbst et al. 2019; Macrae 2022; Shelby et al. 2023; Slattery et al. 2025).”
The Due Process Deficit: Auditing AI Governance in U.S. Higher Education
Teleki, M., Choi, A. S. G., Duray, A., Liu, H., Zhang, J., Dong, X., Da Silva, D., Koenecke, A., & Caverlee, J. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3812328
Sengupta, S. (2026) · World Journal of Engineering and Technology · doi:10.4236/wjet.2026.144053
Algorithmic monoculture and social welfare
(2023) · The Societal Impacts of Algorithmic Decision-Making · doi:10.1145/3603195.3603202
- Authors
- Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J.
- Affiliations
- Data & Society Research Institute (US)
- Haverford College (US)
- University of Utah (US)
- Princeton University (US)
- Venue
- Proceedings of the Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2019-01-29
- Pages
- 59-68
- OpenAlex
- W2897154134
- Cited by
- 1,631
- References
- 68
- Citations by year
- 2026: 518 · 2025: 273 · 2024: 220 · 2023: 197 · 2022: 153 · 2021: 144 · 2020: 87 · 2019: 34 · 2018: 2 · 2017: 1 · 2013: 1 · 2012: 1
- FWCI
- 109.23
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- SSRN Electronic Journal (submittedVersion)
- Funders
- National Science Foundation (IIS-1633400, IIS-1633387, IIS-1633724)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- algorithmic fairness, socio-technical systems, abstraction, modular design, discrimination, justice, due process, decision-making systems, process-oriented design, social actors, science and technology studies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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