Refutation of Shapley Values for XAI -- Additional Evidence
Xuanxiang Huang Joao Marques-Silva
Recent work demonstrated the inadequacy of Shapley values for explainable
artificial intelligence (XAI). Although to disprove a theory a single
counterexample suffices, a possible criticism of earlier work is that the focus
was solely on Boolean classifiers. To address such possible criticism, this
paper demonstrates the inadequacy of Shapley values for families of classifiers
where features are not boolean, but also for families of classifiers for which
multiple classes can be picked. Furthermore, the paper shows that the features
changed in any minimal $l_0$ distance adversarial examples do not include
irrelevant features, thus offering further arguments regarding the inadequacy
of Shapley values for XAI.