From SHAP Scores to Feature Importance Scores
Olivier Letoffe, Xuanxiang Huang, Nicholas Asher, Joao, Marques-Silva

TL;DR
This paper explores the relationship between feature attribution in XAI and voting power indices, proposing new importance scores and analyzing existing indices for better explainability.
Contribution
It establishes a link between feature importance scores and voting power, introduces novel desirable properties for FIS, and evaluates existing power indices within this framework.
Findings
Existing power indices can be adapted as feature importance scores.
Proposed properties help identify suitable power indices for XAI.
Rigorous analysis of power indices reveals their strengths and limitations.
Abstract
A central goal of eXplainable Artificial Intelligence (XAI) is to assign relative importance to the features of a Machine Learning (ML) model given some prediction. The importance of this task of explainability by feature attribution is illustrated by the ubiquitous recent use of tools such as SHAP and LIME. Unfortunately, the exact computation of feature attributions, using the game-theoretical foundation underlying SHAP and LIME, can yield manifestly unsatisfactory results, that tantamount to reporting misleading relative feature importance. Recent work targeted rigorous feature attribution, by studying axiomatic aggregations of features based on logic-based definitions of explanations by feature selection. This paper shows that there is an essential relationship between feature attribution and a priori voting power, and that those recently proposed axiomatic aggregations represent a…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Imbalanced Data Classification Techniques · Big Data and Digital Economy
MethodsLocal Interpretable Model-Agnostic Explanations · Shapley Additive Explanations
