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Measuring Fairness in an Unfair World

Author:

Jonathan HeringtonAuthors Info & Claims

AIES '20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Pages 286 - 292

https://doi.org/10.1145/3375627.3375854

Published: 07 February 2020 Publication History

Abstract

Computer scientists have made great strides in characterizing different measures of algorithmic fairness, and showing that certain measures of fairness cannot be jointly satisfied. In this paper, I argue that the three most popular families of measures - unconditional independence, target-conditional independence and classification-conditional independence - make assumptions that are unsustainable in the context of an unjust world. I begin by introducing the measures and the implicit idealizations they make about the underlying causal structure of the contexts in which they are deployed. I then discuss how these idealizations fall apart in the context of historical injustice, ongoing unmodeled oppression, and the permissibility of using sensitive attributes to rectify injustice. In the final section, I suggest an alternative framework for measuring fairness in the context of existing injustice: distributive fairness.

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Cited By

Venumuddala VPrakash AChaudhuri B(2024)Governing Smart City IoT Interventions: A Complex Adaptive Systems PerspectiveDigital Government: Research and Practice10.1145/36606445:3(1-24)Online publication date: 13-Sep-2024
https://dl.acm.org/doi/10.1145/3660644
Zhang AHosoi A(2024)Structural Interventions and the Dynamics of InequalityProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658952(1014-1030)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658952
Fledderjohann JKnowles BMiller E(2024)Algorithmic Reproductive JusticeProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658903(254-266)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658903
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Index Terms

Measuring Fairness in an Unfair World
1. Computing methodologies
  1. Machine learning
2. Social and professional topics
  1. Computing / technology policy

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cover image ACM Conferences

AIES '20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

February 2020

439 pages

ISBN:9781450371100

DOI:10.1145/3375627

General Chairs:
Annette Markham
Aarhus University | Loyola University
,
Julia Powles
University of Western Australia
,
Toby Walsh
TU Berlin | University of New South Wales | Data61
,
Anne L. Washington
New York University

Copyright © 2020 ACM.

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Published: 07 February 2020

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SIGAI

AIES '20: AAAI/ACM Conference on AI, Ethics, and Society

February 7 - 9, 2020

NY, New York, USA

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Overall Acceptance Rate 61 of 162 submissions, 38%

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Cited By

Venumuddala VPrakash AChaudhuri B(2024)Governing Smart City IoT Interventions: A Complex Adaptive Systems PerspectiveDigital Government: Research and Practice10.1145/36606445:3(1-24)Online publication date: 13-Sep-2024
https://dl.acm.org/doi/10.1145/3660644
Zhang AHosoi A(2024)Structural Interventions and the Dynamics of InequalityProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658952(1014-1030)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658952
Fledderjohann JKnowles BMiller E(2024)Algorithmic Reproductive JusticeProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658903(254-266)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658903
Zhu YWen RWilliams TGrollman DBroadbent EJu WSoh HWilliams T(2024)Robots for Social Justice (R4SJ): Toward a More Equitable Practice of Human-Robot InteractionProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634944(850-859)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610977.3634944
Herington JMcCradden MCreel KBoellaard RJones EJha ARahmim AScott PSunderland JWahl RZuehlsdorff SSaboury B(2023)Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and GovernanceJournal of Nuclear Medicine10.2967/jnumed.123.26611064:10(1509-1515)Online publication date: 24-Aug-2023
https://doi.org/10.2967/jnumed.123.266110
Obi IGray C(2023)Auditing Practitioner Judgment for Algorithmic Fairness Implications2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS)10.1109/ETHICS57328.2023.10154992(01-05)Online publication date: 18-May-2023
https://doi.org/10.1109/ETHICS57328.2023.10154992
Holm S(2023)Egalitarianism and Algorithmic FairnessPhilosophy & Technology10.1007/s13347-023-00607-w36:1Online publication date: 19-Jan-2023
https://doi.org/10.1007/s13347-023-00607-w
Zhang M(2022)Affirmative Algorithms: Relational Equality as Algorithmic FairnessProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533115(495-507)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3531146.3533115
Himmelreich JLim D(2022)AI and Structural InjusticeThe Oxford Handbook of AI Governance10.1093/oxfordhb/9780197579329.013.13(210-231)Online publication date: 18-Aug-2022
https://doi.org/10.1093/oxfordhb/9780197579329.013.13
Pfohl SForyciarz AShah N(2022)An empirical characterization of fair machine learning for clinical risk predictionJournal of Biomedical Informatics10.1016/j.jbi.2020.103621113:COnline publication date: 6-May-2022
https://dl.acm.org/doi/10.1016/j.jbi.2020.103621
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