Project C02: Interactive learning of explainable, situation-adapted decision models
Different strategies are necessary for different situations in machine-based decision-making. The strategy to be used depends, for instance, on the amount of time or information available to make the decision. In Project C02, researchers from the fields of computer science and economics are working on a method to adapt decision-making models to different situations in which experts and users are incorporated in the process of construction. The goal is to enable decision-makers to choose the most suitable model and to be able to retroactively check the decision made.
Research areas: Computer science, Economics
Support staff
Nils Bojack, Paderborn University
Julia Rustemeier, Paderborn University
Luca Manuel Siekermann, Paderborn University
Publications
The Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks
A. Lebedeva, J. Kornowicz, O. Lammert, J. Papenkordt, in: Artificial Intelligence in HCI, 2023.
Aggregating Human Domain Knowledge for Feature Ranking
J. Kornowicz, K. Thommes, Artificial Intelligence in HCI (2023).
Probabilistic Scoring Lists for Interpretable Machine Learning
J.M. Hanselle, J. Fürnkranz, E. Hüllermeier, in: Discovery Science, Springer Nature Switzerland, Cham, 2023.
Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain
J.M. Hanselle, J. Kornowicz, S. Heid, K. Thommes, E. Hüllermeier, in: M. Leyer, J. Wichmann (Eds.), LWDA’23: Learning, Knowledge, Data, Analysis. , 2023.
Learning decision catalogues for situated decision making: The case of scoring systems
S. Heid, J.M. Hanselle, J. Fürnkranz, E. Hüllermeier, International Journal of Approximate Reasoning 171 (2024).
Towards a Computational Architecture for Co-Constructive Explainable Systems
M. Booshehri, H. Buschmeier, P. Cimiano, S. Kopp, J. Kornowicz, O. Lammert, M. Matarese, D. Mindlin, A.S. Robrecht, A.-L. Vollmer, P. Wagner, B. Wrede, in: Proceedings of the 2024 Workshop on Explainability Engineering, ACM, 2024, pp. 20–25.
Algorithm, Expert, or Both? Evaluating the Role of Feature Selection Methods on User Preferences and Reliance
J. Kornowicz, K. Thommes, ArXiv (2024).
Learning decision catalogues for situated decision making: The case of scoring systems
S. Heid, J.M. Hanselle, J. Fürnkranz, E. Hüllermeier, International Journal of Approximate Reasoning 171 (2024).
Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems
S. Heid, J. Kornowicz, J.M. Hanselle, E. Hüllermeier, K. Thommes, in: PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE, 2024, p. 233.
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