AGENTIC-SYSTEMSApril 20, 20261 min read10 views

Towards Rigorous Explainability by Feature Attribution

The paper discusses the lack of rigor in current non-symbolic XAI methods like SHAP and advocates for symbolic methods as a more reliable alternative for feature attribution in high-stakes AI applications.

Computer Science > Artificial Intelligence

Title:Towards Rigorous Explainability by Feature Attribution

View PDF HTML (experimental)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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Source: arXiv cs.AI Recent

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