2023/02/16 by Xuanxiang Huang, Huang, Xuanxiang, João Marques‐Silva +1 · 12 citations
Computer Science · Mathematics · Psychology · #Adversarial Robustness in Machine Learning #Algorithm #Argument (complex analysis) #Artificial intelligence #Attribution #Computation #Computer science #Explainable Artificial Intelligence (XAI) #Feature (linguistics) #Game theory #Machine Learning and Data Classification #Machine learning #Mathematical economics #Mathematics #Psychology #Shapley value #Theoretical computer science #Yield (engineering)
paper · pdf · doi:10.48550/arxiv.2302.08160
published in HAL (Le Centre pour la Communication Scientifique Directe) (Centre National de la Recherche Scientifique)
openalex publication_date 2023/02/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
This paper develops a rigorous argument for why the use of Shapley values in explainable AI (XAI) will necessarily yield provably misleading information about the relative importance of features for predictions. Concretely, this paper demonstrates that there exist classifiers, and associated predictions, for which the relative importance of features determined by the Shapley values will incorrectly assign more importance to features that are provably irrelevant for the prediction, and less importance to features that are provably relevant for the prediction. The paper also argues that, given recent complexity results, the existence of efficient algorithms for the computation of rigorous feature attribution values in the case of some restricted classes of classifiers should be deemed unlikely at best.