Explainability is NOT a Game

• URL: http://arxiv.org/abs/2307.07514v2
• Date: Fri, 9 Feb 2024 13:29:14 GMT
• Title: Explainability is NOT a Game
• Authors: Joao Marques-Silva and Xuanxiang Huang
• Abstract summary: XAI aims to help human decision-makers in understanding complex machine learning (ML) models. One of the hallmarks of XAI are measures of relative feature importance, which are theoretically justified through the use of Shapley values. This paper builds on recent work and offers a simple argument for why Shapley values can provide misleading measures of relative feature importance.
• Score: 4.483306836710804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding complex machine learning (ML) models. One of the hallmarks of XAI are measures of relative feature importance, which are theoretically justified through the use of Shapley values. This paper builds on recent work and offers a simple argument for why Shapley values can provide misleading measures of relative feature importance, by assigning more importance to features that are irrelevant for a prediction, and assigning less importance to features that are relevant for a prediction. The significance of these results is that they effectively challenge the many proposed uses of measures of relative feature importance in a fast-growing range of high-stakes application domains.

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