Towards Rigorous Explainability by Feature Attribution

2026-04-17

AI Summary

This work addresses the theoretical shortcomings of prevailing post-hoc feature attribution methods—such as SHAP—which often lack formal guarantees and can mislead human decision-making in high-stakes settings. To overcome this limitation, the paper introduces a novel paradigm grounded in symbolic explainable artificial intelligence (XAI), leveraging formal, verifiable symbolic reasoning to reconstruct feature importance assignments. By integrating formal verification with feature attribution analysis, the proposed framework establishes a theoretically sound and certifiable approach to interpretability. This integration significantly enhances the reliability and trustworthiness of XAI in safety-critical applications, offering a rigorous and verifiable pathway for generating explanations in high-risk machine learning systems.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Feature Construction/Reformulation
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• explainable artificial intelligence
• feature attribution
• Shapley values
• rigor
• symbolic methods

Innovation

• rigorous explainability
• symbolic methods
• feature attribution
• Shapley values
• explainable AI