abstract

The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems

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Kathleen Creel,

Deborah HellmanAuthors Info & Claims

FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Page 816

https://doi.org/10.1145/3442188.3445942

Published: 01 March 2021 Publication History

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Abstract

Automated decision-making systems implemented in public life are typically standardized. One algorithmic decision-making system can replace thousands of human deciders. Each of the humans so replaced had her own decision-making criteria: some good, some bad, and some arbitrary. Is such arbitrariness of moral concern?

We argue that an isolated arbitrary decision need not morally wrong the individual whom it misclassifies. However, if the same algorithms are applied across a public sphere, such as hiring or lending, a person could be excluded from a large number of opportunities. This harm persists even when the automated decision-making systems are "fair" on standard metrics of fairness. We argue that such arbitrariness at scale is morally problematic and propose technically informed solutions that can lessen the impact of algorithms at scale and so mitigate or avoid the moral harms we identify.

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Gomez JMachado CPaes LCalmon F(2024)Algorithmic Arbitrariness in Content ModerationProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659036(2234-2253)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659036
Naudts L(2024)The Digital Faces of Oppression and Domination: A Relational and Egalitarian Perspective on the Data-driven Society and its RegulationProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658934(701-712)Online publication date: 3-Jun-2024
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Watson DMökander JFloridi L(2024)Competing narratives in AI ethics: a defense of sociotechnical pragmatismAI & SOCIETY10.1007/s00146-024-02128-2Online publication date: 27-Dec-2024
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The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems

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FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

March 2021

899 pages

ISBN:9781450383097

DOI:10.1145/3442188

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 01 March 2021

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March 3 - 10, 2021

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https://dl.acm.org/doi/10.1145/3630106.3659036
Naudts L(2024)The Digital Faces of Oppression and Domination: A Relational and Egalitarian Perspective on the Data-driven Society and its RegulationProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658934(701-712)Online publication date: 3-Jun-2024
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Watson DMökander JFloridi L(2024)Competing narratives in AI ethics: a defense of sociotechnical pragmatismAI & SOCIETY10.1007/s00146-024-02128-2Online publication date: 27-Dec-2024
https://doi.org/10.1007/s00146-024-02128-2
Hull G(2023)Unlearning Descartes: Sentient AI is a Political ProblemJournal of Social Computing10.23919/JSC.2023.00204:3(193-204)Online publication date: Sep-2023
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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
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How do Undergraduate Students Reason about Ethical and Algorithmic Decision-Making?

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