A scoring system is a simple decision model that checks a set of features, adds a certain number of points to a total score for each feature that is satisfied, and finally makes a decision by comparing the total score to a threshold. Scoring systems have a long history of active use in safety-critical domains such as healthcare and justice, where they provide guidance for making objective and accurate decisions. Given their genuine interpretability, the idea of learning scoring systems from data is obviously appealing from the perspective of explainable AI. In this paper, we propose a practically motivated extension of scoring systems called probabilistic scoring lists (PSL), as well as a method for learning PSLs from data. Instead of making a deterministic decision, a PSL represents uncertainty in the form of probability distributions, or, more generally, probability intervals. Moreover, in the spirit of decision lists, a PSL evaluates features one by one and stops as soon as a decision can be made with enough confidence. To evaluate our approach, we conduct a case study in the medical domain.

翻译：评分系统是一种简单的决策模型，它通过检查一组特征，为每个满足条件的特征向总分添加特定点数，最终通过将总分与阈值比较来做出决策。评分系统在医疗和司法等安全关键领域有着长期活跃的应用历史，为做出客观准确的决策提供指导。鉴于其固有的可解释性，从数据中学习评分系统的思想显然对可解释人工智能具有吸引力。本文提出一种具有实践意义的评分系统扩展形式——概率评分列表（PSL），以及从数据中学习PSL的方法。与确定性决策不同，PSL以概率分布（或更一般地，概率区间）的形式表示不确定性。此外，秉承决策列表的思想，PSL逐个评估特征，并在能够以足够置信度做出决策时立即停止评估。为验证本方法，我们在医疗领域开展了案例研究。