MSL: Multi-class Scoring Lists for Interpretable Incremental Decision-Making

Stefan Heid, Jaroslaw Kornowicz, Jonas Hanselle, Kirsten Thommes, Eyke Hüllermeier

Abstract: A scoring list is a sequence of simple decision models, where features are incrementally evaluated and scores of satisfied features are summed to be used for threshold-based decisions or for calculating class probabilities. In this paper, we introduce a new multi-class variant and compare it against previously introduced binary classification variants for incremental decisions, as well as multi-class variants for classical decision-making using all features. Furthermore, we introduce a new multi-class dataset to assess collaborative human-machine decision-making, which is suitable for user studies with non-expert participants. We demonstrate the usefulness of our approach by evaluating predictive performance and compared to the performance of participants without AI help.

External IDs:dblp:conf/xai/HeidKHTH25