research-article

Robustness Implies Fairness in Causal Algorithmic Recourse

Authors:

Ahmad-Reza Ehyaei,

Amir-Hossein Karimi,

Bernhard Schoelkopf,

Setareh MaghsudiAuthors Info & Claims

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

Pages 984 - 1001

https://doi.org/10.1145/3593013.3594057

Published: 12 June 2023 Publication History

Abstract

Algorithmic recourse discloses the internal procedures of a black-box decision process where decisions have significant consequences by providing recommendations to empower beneficiaries to achieve a more favorable outcome. To ensure an effective remedy, suggested interventions must not only be cost-effective but also robust and fair. To that end, it is essential to provide similar explanations to similar individuals. This study explores the concept of individual fairness and adversarial robustness in causal algorithmic recourse and addresses the challenge of achieving both. To resolve the challenges, we propose a new framework for defining adversarially robust recourse. That setting observes the protected feature as a pseudometric and demonstrates that individual fairness is a special case of adversarial robustness. Finally, we introduce the fair robust recourse problem and establish solutions to achieve both desirable properties both theoretically and empirically.

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Cited By

Fonseca JBell AAbrate CBonchi FStoyanovich J(2023)Setting the Right Expectations: Algorithmic Recourse Over TimeProceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3617694.3623251(1-11)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3617694.3623251

Index Terms

Robustness Implies Fairness in Causal Algorithmic Recourse
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Causal reasoning and diagnostics
2. Information systems
  1. Information systems applications
    1. Decision support systems

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

June 2023

1929 pages

ISBN:9798400701924

DOI:10.1145/3593013

Copyright © 2023 ACM.

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 the author(s) 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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Published: 12 June 2023

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FAccT '23

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

June 12 - 15, 2023

IL, Chicago, USA

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Cited By

Fonseca JBell AAbrate CBonchi FStoyanovich J(2023)Setting the Right Expectations: Algorithmic Recourse Over TimeProceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3617694.3623251(1-11)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3617694.3623251

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