research-article

On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations

Authors:

Emanuele Albini,

Shubham Sharma,

Saumitra Mishra,

Danial Dervovic,

Daniele MagazzeniAuthors Info & Claims

AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society

Pages 411 - 431

https://doi.org/10.1145/3600211.3604676

Published: 29 August 2023 Publication History

Abstract

Explainable Artificial Intelligence (XAI) has received widespread interest in recent years, and two of the most popular types of explanations are feature attributions, and counterfactual explanations. These classes of approaches have been largely studied independently and the few attempts at reconciling them have been primarily empirical. This work establishes a clear theoretical connection between game-theoretic feature attributions, focusing on but not limited to SHAP, and counterfactuals explanations. After motivating operative changes to Shapley values based feature attributions and counterfactual explanations, we prove that, under conditions, they are in fact equivalent. We then extend the equivalency result to game-theoretic solution concepts beyond Shapley values. Moreover, through the analysis of the conditions of such equivalence, we shed light on the limitations of naively using counterfactual explanations to provide feature importances. Experiments on three datasets quantitatively show the difference in explanations at every stage of the connection between the two approaches and corroborate the theoretical findings.

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Cited By

An SCao Y(2024)Counterfactual Explanation at Will, with Zero Privacy LeakageProceedings of the ACM on Management of Data10.1145/36549332:3(1-29)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654933
Jakubik JKwaśnicka HLi XHandl J(2024)Drawing Attributions From Evolved CounterfactualsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664122(1582-1589)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664122
Manganini CPrimiero G(2024)Reasoning With and About BiasPerspectives on Logics for Data-driven Reasoning10.1007/978-3-031-77892-6_7(127-154)Online publication date: 6-Nov-2024
https://doi.org/10.1007/978-3-031-77892-6_7

Index Terms

On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations

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cover image ACM Conferences

AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society

August 2023

1026 pages

ISBN:9798400702310

DOI:10.1145/3600211

Editors:
Francesca Rossi
IBM
,
Sanmay Das
George Mason University
,
Jenny Davis
Australian National University
,
Kay Firth-Butterfield
Centre for Trustworthy Technology
,
Alex John
London, Carnegie Mellon University

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Cited By

An SCao Y(2024)Counterfactual Explanation at Will, with Zero Privacy LeakageProceedings of the ACM on Management of Data10.1145/36549332:3(1-29)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654933
Jakubik JKwaśnicka HLi XHandl J(2024)Drawing Attributions From Evolved CounterfactualsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664122(1582-1589)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664122
Manganini CPrimiero G(2024)Reasoning With and About BiasPerspectives on Logics for Data-driven Reasoning10.1007/978-3-031-77892-6_7(127-154)Online publication date: 6-Nov-2024
https://doi.org/10.1007/978-3-031-77892-6_7

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