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Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification

Authors:

Emmanouil Krasanakis,

Eleftherios Spyromitros-Xioufis,

Symeon Papadopoulos,

Yiannis KompatsiarisAuthors Info & Claims

WWW '18: Proceedings of the 2018 World Wide Web Conference

Pages 853 - 862

https://doi.org/10.1145/3178876.3186133

Published: 23 April 2018 Publication History

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Abstract

Machine learning bias and fairness have recently emerged as key issues due to the pervasive deployment of data-driven decision making in a variety of sectors and services. It has often been argued that unfair classifications can be attributed to bias in training data, but previous attempts to 'repair' training data have led to limited success. To circumvent shortcomings prevalent in data repairing approaches, such as those that weight training samples of the sensitive group (e.g. gender, race, financial status) based on their misclassification error, we present a process that iteratively adapts training sample weights with a theoretically grounded model. This model addresses different kinds of bias to better achieve fairness objectives, such as trade-offs between accuracy and disparate impact elimination or disparate mistreatment elimination. We show that, compared to previous fairness-aware approaches, our methodology achieves better or similar trades-offs between accuracy and unfairness mitigation on real-world and synthetic datasets.

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Index Terms

Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification

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cover image ACM Other conferences

WWW '18: Proceedings of the 2018 World Wide Web Conference

April 2018

2000 pages

ISBN:9781450356398

General Chairs:
Pierre-Antoine Champin
Universitè Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

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Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 23 April 2018

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WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

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WWW '18 Paper Acceptance Rate 170 of 1,155 submissions, 15%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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https://doi.org/10.1109/TPAMI.2024.3487254
Mou NYue XZhao LWang Q(2025)Fairness is essential for robustness: fair adversarial training by identifying and augmenting hard examplesFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-3587-119:3Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1007/s11704-024-3587-1
Roberts-Licklider KTrafalis T(2025)Fairness in Optimization and ML: A Survey Part 2Dynamics of Information Systems10.1007/978-3-031-81010-7_17(273-290)Online publication date: 26-Feb-2025
https://doi.org/10.1007/978-3-031-81010-7_17
Sarridis IKoutlis CPapadopoulos SDiou C(2025)Towards Fair Face Verification: An In-depth Analysis of Demographic BiasesMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74630-7_14(194-208)Online publication date: 8-Feb-2025
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Trigo AStein NPaulo Belfo F(2024)Strategies to improve fairness in artificial intelligence:A systematic literature reviewEducation for Information10.3233/EFI-240045(1-24)Online publication date: 25-Jul-2024
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