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What Should We Do when Our Ideas of Fairness Conflict?

Author:

Manish RaghavanAuthors Info & Claims

Communications of the ACM, Volume 67, Issue 1

Pages 88 - 97

https://doi.org/10.1145/3587930

Published: 21 December 2023 Publication History

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Abstract

Standards for fair decision making could help us develop algorithms that comport with our consensus views; however, algorithmic fairness has its limits.

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Cited By

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Cavique L(2024)Implications of causality in artificial intelligenceFrontiers in Artificial Intelligence10.3389/frai.2024.14397027Online publication date: 21-Aug-2024
https://doi.org/10.3389/frai.2024.1439702
Somerstep SRitov YSun Y(2024)Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group FairnessProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658929(616-630)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658929
Criscuolo CDolci T(2024)Exploring Fairness Interpretability with FairnessFriend: A Chatbot Solution2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW61823.2024.00037(246-253)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDEW61823.2024.00037

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What Should We Do when Our Ideas of Fairness Conflict?

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Published In

Communications of the ACM Volume 67, Issue 1

January 2024

122 pages

EISSN:1557-7317

DOI:10.1145/3638509

Editor:
James Larus
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

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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Published: 21 December 2023

Published in CACM Volume 67, Issue 1

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Cavique L(2024)Implications of causality in artificial intelligenceFrontiers in Artificial Intelligence10.3389/frai.2024.14397027Online publication date: 21-Aug-2024
https://doi.org/10.3389/frai.2024.1439702
Somerstep SRitov YSun Y(2024)Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group FairnessProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658929(616-630)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658929
Criscuolo CDolci T(2024)Exploring Fairness Interpretability with FairnessFriend: A Chatbot Solution2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW61823.2024.00037(246-253)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDEW61823.2024.00037

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Recommendations

Airtime Fairness for IEEE 802.11 Multirate Networks

Optimal CWmin selection for achieving proportional fairness in multi-rate 802.11e WLANs: test-bed implementation and evaluation

Achieving per-station fairness in IEEE 802.11 wireless LANs

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