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Accounting for Model Uncertainty in Algorithmic Discrimination

Authors:

Preethi Lahoti,

Krishna P. GummadiAuthors Info & Claims

AIES '21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

Pages 336 - 345

https://doi.org/10.1145/3461702.3462630

Published: 30 July 2021 Publication History

Abstract

Traditional approaches to ensure group fairness in algorithmic decision making aim to equalize "total" error rates for different subgroups in the population. In contrast, we argue that the fairness approaches should instead focus only on equalizing errors arising due to model uncertainty (a.k.a epistemic uncertainty), caused due to lack of knowledge about the best model or due to lack of data. In other words, our proposal calls for ignoring the errors that occur due to uncertainty inherent in the data, i.e., aleatoric uncertainty. We draw a connection between predictive multiplicity and model uncertainty and argue that the techniques from predictive multiplicity could be used to identify errors made due to model uncertainty. We propose scalable convex proxies to come up with classifiers that exhibit predictive multiplicity and empirically show that our methods are comparable in performance and up to four orders of magnitude faster than the current state-of-the-art. We further pro- pose methods to achieve our goal of equalizing group error rates arising due to model uncertainty in algorithmic decision making and demonstrate the effectiveness of these methods using synthetic and real-world datasets

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Cited By

Agudelo-Cardona VBarbosa IVallejo MBahamón-Cortés NDelgado-Trejos E(2023)Incertidumbre epistémica y aleatoria en soft metrología: una perspectiva desde el aseguramiento de la validez de los resultadosIngeniería10.14483/23448393.1888328:2(e18883)Online publication date: 29-Apr-2023
https://doi.org/10.14483/23448393.18883
Watson-Daniels JBarocas SHofman JChouldechova A(2023)Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource ConstraintsProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3593998(297-311)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3593998
Vallejo MBahamón NRossi LDelgado-Trejos E(2023)Soft MetrologyHandbook of Metrology and Applications10.1007/978-981-99-2074-7_67(1239-1269)Online publication date: 24-Aug-2023
https://doi.org/10.1007/978-981-99-2074-7_67
Show More Cited By

Index Terms

Accounting for Model Uncertainty in Algorithmic Discrimination
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods
2. Social and professional topics

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Published In

cover image ACM Conferences

AIES '21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

July 2021

1077 pages

ISBN:9781450384735

DOI:10.1145/3461702

Program Chairs:
Marion Fourcade
University of California Berkeley, USA
,
Benjamin Kuipers
University of Michigan, USA
,
Seth Lazar
Australian National University, Australia
,
Deirdre Mulligan
University of California Berkeley, USA

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 July 2021

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European Research Council

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AIES '21

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SIGAI

AIES '21: AAAI/ACM Conference on AI, Ethics, and Society

May 19 - 21, 2021

Virtual Event, USA

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Overall Acceptance Rate 61 of 162 submissions, 38%

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Cited By

Agudelo-Cardona VBarbosa IVallejo MBahamón-Cortés NDelgado-Trejos E(2023)Incertidumbre epistémica y aleatoria en soft metrología: una perspectiva desde el aseguramiento de la validez de los resultadosIngeniería10.14483/23448393.1888328:2(e18883)Online publication date: 29-Apr-2023
https://doi.org/10.14483/23448393.18883
Watson-Daniels JBarocas SHofman JChouldechova A(2023)Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource ConstraintsProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3593998(297-311)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3593998
Vallejo MBahamón NRossi LDelgado-Trejos E(2023)Soft MetrologyHandbook of Metrology and Applications10.1007/978-981-99-2074-7_67(1239-1269)Online publication date: 24-Aug-2023
https://doi.org/10.1007/978-981-99-2074-7_67
Oh SUstun BMcAuley JKumar SAl Hasan MXiong L(2022)Rank List Sensitivity of Recommender Systems to Interaction PerturbationsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557425(1584-1594)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557425
Stone RRavikumar NBulpitt AHogg D(2022)Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00327(2897-2904)Online publication date: Jun-2022
https://doi.org/10.1109/CVPRW56347.2022.00327
Fabris AMessina SSilvello GSusto G(2022)Algorithmic fairness datasets: the story so farData Mining and Knowledge Discovery10.1007/s10618-022-00854-z36:6(2074-2152)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s10618-022-00854-z
Vallejo MBahamón NRossi LDelgado-Trejos E(2022)Soft MetrologyHandbook of Metrology and Applications10.1007/978-981-19-1550-5_67-1(1-31)Online publication date: 14-Dec-2022
https://doi.org/10.1007/978-981-19-1550-5_67-1

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