research-article

Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness

Authors:

Brianna Richardson,

Prasanna Sattigeri,

Karthikeyan Natesan Ramamurthy,

Amit Dhurandhar,

Juan E. GilbertAuthors Info & Claims

FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

Pages 736 - 752

https://doi.org/10.1145/3593013.3594039

Published: 12 June 2023 Publication History

Abstract

Commensurate with the rise in algorithmic bias research, myriad algorithmic bias mitigation strategies have been proposed in the literature. Nonetheless, many voice concerns about the lack of transparency that accompanies mitigation methods and the paucity of mitigation methods that satisfy protocol and data limitations of practitioners. Influence functions from robust statistics provide a novel opportunity to overcome both issues. Previous work demonstrates the power of influence functions to improve fairness outcomes. This work proposes a novel family of fairness solutions, coined influential fairness (IF), that is human-understandable and also agnostic to the underlying machine learning model and choice of fairness metric. We conduct an investigation of practitioner profiles and design mitigation methods for practitioners whose limitations discourage them from utilizing existing bias mitigation methods.

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Cited By

Park TJung SChun SMoon T(2024)FairDRO: Group fairness regularization via classwise robust optimizationNeural Networks10.1016/j.neunet.2024.106891(106891)Online publication date: Nov-2024
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Liu FDu XDing HQian J(2024)Towards robust neural networks: Exploring counterfactual causality-based repairExpert Systems with Applications10.1016/j.eswa.2024.125082257(125082)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.125082

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FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

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DOI:10.1145/3593013

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FAccT '23: the 2023 ACM Conference on Fairness, Accountability, and Transparency

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Park TJung SChun SMoon T(2024)FairDRO: Group fairness regularization via classwise robust optimizationNeural Networks10.1016/j.neunet.2024.106891(106891)Online publication date: Nov-2024
https://doi.org/10.1016/j.neunet.2024.106891
Liu FDu XDing HQian J(2024)Towards robust neural networks: Exploring counterfactual causality-based repairExpert Systems with Applications10.1016/j.eswa.2024.125082257(125082)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.125082

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