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

MCML Authors

Jonas Hanselle

→ Group Eyke Hüllermeier
Artificial Intelligence and Machine Learning

Eyke Hüllermeier

Prof. Dr.

Principal Investigator

Artificial Intelligence and Machine Learning

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.

inproceedings HKH+25