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Fair Prediction with Endogenous Behavior

Authors:

Rakesh VohraAuthors Info & Claims

EC '20: Proceedings of the 21st ACM Conference on Economics and Computation

Pages 677 - 678

https://doi.org/10.1145/3391403.3399473

Published: 13 July 2020 Publication History

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Abstract

There is great interest in whether machine learning algorithms deployed in consequential domains (e.g. in criminal justice) treat different demographic groups "fairly." However, there are several proposed notions of fairness, typically mutually incompatible. Using criminal justice as an example, we study a model in which society chooses an incarceration rule. Agents of different demographic groups differ in their outside options (e.g. opportunity for legal employment) and decide whether to commit crimes. We show that equalizing type I and type II errors across groups is consistent with the goal of minimizing the overall crime rate; other popular notions of fairness are not.

Cited By

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Finocchiaro J(2024)Using Property Elicitation to Understand the Impacts of Fairness RegularizersProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658540(62-73)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658540
Caton SHaas C(2024)Fairness in Machine Learning: A SurveyACM Computing Surveys10.1145/361686556:7(1-38)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3616865
Charpentier ACharpentier A(2024)Group FairnessInsurance, Biases, Discrimination and Fairness10.1007/978-3-031-49783-4_8(309-355)Online publication date: 14-May-2024
https://doi.org/10.1007/978-3-031-49783-4_8
Show More Cited By

Index Terms

Fair Prediction with Endogenous Behavior
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Algorithmic game theory and mechanism design
      1. Algorithmic game theory

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Information

Published In

EC '20: Proceedings of the 21st ACM Conference on Economics and Computation

July 2020

937 pages

ISBN:9781450379755

DOI:10.1145/3391403

General Chairs:
Péter Biró
Hungarian Academy of Sciences
,
Jason Hartline
Northwestern University
,
Program Chairs:
Michael Ostrovsky
Stanford University
,
Ariel Procaccia
Harvard University

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

New York, NY, United States

Publication History

Published: 13 July 2020

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EC '20

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SIGecom

EC '20: The 21st ACM Conference on Economics and Computation

July 13 - 17, 2020

Virtual Event, Hungary

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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Cited By

View all

Finocchiaro J(2024)Using Property Elicitation to Understand the Impacts of Fairness RegularizersProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658540(62-73)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658540
Caton SHaas C(2024)Fairness in Machine Learning: A SurveyACM Computing Surveys10.1145/361686556:7(1-38)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3616865
Charpentier ACharpentier A(2024)Group FairnessInsurance, Biases, Discrimination and Fairness10.1007/978-3-031-49783-4_8(309-355)Online publication date: 14-May-2024
https://doi.org/10.1007/978-3-031-49783-4_8
Charpentier A(2024)Quantifying Fairness and Discrimination in Predictive ModelsMachine Learning for Econometrics and Related Topics10.1007/978-3-031-43601-7_3(37-77)Online publication date: 2-Jun-2024
https://doi.org/10.1007/978-3-031-43601-7_3
Li YChen HXu SGe YTan JLiu SZhang Y(2023)Fairness in Recommendation: Foundations, Methods, and ApplicationsACM Transactions on Intelligent Systems and Technology10.1145/361030214:5(1-48)Online publication date: 27-Jul-2023
https://dl.acm.org/doi/10.1145/3610302
Acharya KArunachaleswaran EKannan SRoth AZiani J(2023)Wealth Dynamics Over Generations: Analysis and Interventions2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)10.1109/SaTML54575.2023.00013(42-57)Online publication date: Mar-2023
https://doi.org/10.1109/SaTML54575.2023.00013
Niu MKannan SRoth AVohra R(2022)Best vs. All: Equity and Accuracy of Standardized Test Score ReportingProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533121(574-586)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3531146.3533121
Patty JPenn E(2022)Algorithmic Fairness and Statistical DiscriminationPhilosophy Compass10.1111/phc3.1289118:1Online publication date: 19-Dec-2022
https://doi.org/10.1111/phc3.12891
Finocchiaro JMaio RMonachou FPatro GRaghavan MStoica ATsirtsis S(2021)Bridging Machine Learning and Mechanism Design towards Algorithmic FairnessProceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency10.1145/3442188.3445912(489-503)Online publication date: 3-Mar-2021
https://dl.acm.org/doi/10.1145/3442188.3445912
Kearns MRoth A(2020)Ethical algorithm designACM SIGecom Exchanges10.1145/3440959.344096618:1(31-36)Online publication date: 2-Dec-2020
https://dl.acm.org/doi/10.1145/3440959.3440966
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