Probabilistic Scoring Lists for Interpretable Machine Learning

TL;DR

This paper introduces probabilistic scoring lists (PSL), an extension of scoring systems that incorporate uncertainty and probabilistic decision-making, aiming to improve interpretability and decision confidence in safety-critical applications.

Contribution

The paper proposes PSL, a novel probabilistic extension of scoring systems, and presents a method to learn these from data, enhancing interpretability with uncertainty quantification.

Findings

Successful case study in the medical domain

Demonstrated ability to evaluate uncertainty in decisions

Improved interpretability with probabilistic outputs

Abstract

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.