揭示规则模型解释不可靠的根源，提出分析工具。
On Trustworthy Rule-Based Models and Explanations
- 识别规则模型中负重叠与冗余等缺陷
- 发现主流学习工具生成的规则集普遍存在缺陷
- 适合关注模型可信解释的研究者与从业者
机器学习中的一个重要任务是为模型预测提供可解释性。在高风险领域，解释的严谨性尤为关键，错误的解释可能误导人类决策者。尽管可解释性概念仍不明确，规则模型（如决策树、规则列表和集合）仍被广泛用于高风险场景。本文揭示了规则模型解释可靠性与固有缺陷之间的关联，包括负重叠和多种冗余形式。论文提出了分析这些缺陷的算法，并指出当前广泛应用的规则学习工具生成的规则集普遍存在至少一种负面特性。
原文摘要 · Abstract (English)
A task of interest in machine learning (ML) is that of ascribing explanations to the predictions made by ML models. Furthermore, in domains deemed high risk, the rigor of explanations is paramount. Indeed, incorrect explanations can and will mislead human decision makers. As a result, and even if interpretability is acknowledged as an elusive concept, so-called interpretable models are employed ubiquitously in high-risk uses of ML and data mining (DM). This is the case for rule-based ML models, which encompass decision trees, diagrams, sets and lists. This paper relates explanations with well-known undesired facets of rule-based ML models, which include negative overlap and several forms of redundancy. The paper develops algorithms for the analysis of these undesired facets of rule-based systems, and concludes that well-known and widely used tools for learning rule-based ML models will induce rule sets that exhibit one or more negative facets.
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