Limitations of SHAP Scores for Feature Attribution in Machine Learning Models

The core message of this paper is that the issues with SHAP scores for feature attribution are solely attributed to the characteristic functions used in earlier works, and not to the theoretical foundations of Shapley values. The paper proposes several novel characteristic functions that respect key properties to ensure SHAP scores do not provide misleading information about relative feature importance.