Towards Rigorous Explainability by Feature Attribution

• URL: http://arxiv.org/abs/2604.15898v1
• Date: Fri, 17 Apr 2026 09:56:17 GMT
• Title: Towards Rigorous Explainability by Feature Attribution
• Abstract summary: Non-symbolic methods lack rigor and can mislead human decision-makers.<n>In high-stakes uses of machine learning, the lack of rigor is especially problematic.<n>This paper overviews the efforts towards using rigorous symbolic methods of XAI as an alternative to non-symbolic approaches.
• Score: 5.407319151576265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such methods lack rigor and can mislead human decision-makers. In high-stakes uses of ML, the lack of rigor is especially problematic. One prime example of provable lack of rigor is the adoption of Shapley values in explainable artificial intelligence (XAI), with the tool SHAP being a ubiquitous example. This paper overviews the ongoing efforts towards using rigorous symbolic methods of XAI as an alternative to non-rigorous non-symbolic approaches, concretely for assigning relative feature importance.

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