用符号方法替代模糊的特征归因，提升模型解释的严谨性。
Towards Rigorous Explainability by Feature Attribution
- 提出用符号逻辑替代非严格非符号方法进行特征重要性分析
- 指出当前广泛使用的SHAP值存在解释不严谨的问题
- 适合关注高风险场景下模型可解释性的研究人员
过去十年，非符号方法一直是解释复杂机器学习模型的首选。然而，这些方法缺乏严谨性，可能误导人类决策者。在高风险应用场景中，这种不严谨尤为严重。一个典型的例子是可解释人工智能（XAI）中对谢林值（Shapley values）的使用，其中工具SHAP被普遍采用。本文综述了当前转向更严谨的符号化XAI方法的努力，旨在为特征重要性分配提供更可靠的依据。
原文摘要 · Abstract (English)
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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