It's Not Fairness, and It's Not Fair: The Failure of Distributional Equality and the Promise of Relational Equality in Complete-Information Hiring Games
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Abstract (arXiv)
Existing efforts to formulate computational definitions of fairness have largely focused on distributional notions of equality, where equality is defined by the resources or decisions given to individuals in the system. Yet existing discrimination and injustice is often the result of unequal social relations, rather than an unequal distribution of resources. Here, we show how optimizing for existing computational and economic definitions of fairness and equality fail to prevent unequal social relations. To do this, we provide an example of a self-confirming equilibrium in a simple hiring market that is relationally unequal but satisfies existing distributional notions of fairness. In doing so, we introduce a notion of blatant relational unfairness for complete-information games, and discuss how this definition helps initiate a new approach to incorporating relational equality into computational systems.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 84 references that are in the map. First posted on arXiv 12 Sep 2022.
Builds on
23 of 84 references.
References · 23
- 2016Equality of Opportunity in Supervised Learningcited by 2,007
- 2017Counterfactual Fairnesscited by 804
- 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
- 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
- 2018Delayed Impact of Fair Machine Learningcited by 184
- 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 174
- 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 149
- 2019Fairness in Deep Learning: A Computational Perspectivecited by 123
- 201850 Years of Test (Un)fairness: Lessons for Machine Learningcited by 101
- 2019Problem Formulation and Fairnesscited by 85
- 2021Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Developmentcited by 85
- 2018Online Learning with an Unknown Fairness Metriccited by 62
11 more
- 2018A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunitycited by 53
- 2019Metric Learning for Individual Fairnesscited by 52
- 2018Racial categories in machine learningcited by 48
- 2018Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Makingcited by 46
- 2018From Fair Decision Making to Social Equalitycited by 36
- 2018Fair Algorithms for Learning in Allocation Problemscited by 29
- 2019Fairness in representation: quantifying stereotyping as a representational harmcited by 26
- 2017Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessmentcited by 16
- 2019An Algorithmic Framework for Fairness Elicitationcited by 16
- 2020Reflexive Design for Fairness and Other Human Values in Formal Modelscited by 11
- 2019Gaps in Information Access in Social Networkscited by 9
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.
3 papers.
- 2022Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?cited by 91
- 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
- 2025Last-Iterate Convergence of No-Regret Learning for Equilibria in Bargaining Gamescited by 0
- 2025Last-Iterate Convergence of No-Regret Learning for Equilibria in Bargaining Games3 Jul 2025
- 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools29 Sep 2023
- 2022Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?25 Nov 2022