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

Heid, Stefan ; Kornowicz, Jaroslaw ; Hanselle, Jonas ; Thommes, Kirsten und Hüllermeier, Eyke (2025): MSL: Multi-class Scoring Lists for Interpretable Incremental Decision-Making. The 3rd World Conference on Explainable Artificial Intelligence (xAI 2025), Istanbul, Turkey, 9. -11. July 2025. In: Explainable Artificial Intelligence, Vol. 2578 Springer, Cham. pp. 117-139

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.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISBN: 978-3-032-08327-2
• ISSN: 1865-0929
• Language: English