Feb. 12, 2024, 5:43 a.m. | Joao Marques-Silva Xuanxiang Huang

cs.LG updates on arXiv.org arxiv.org

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 …

artificial artificial intelligence cs.ai cs.lg decision explainability explainable artificial intelligence feature game human importance intelligence machine machine learning makers paper simple through understanding values work xai

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