Interactive learning of explainable, situation-adapted decision models (Project C02)

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.

Kornowicz, J., Thommes, K., (2023) Aggregating Human Domain Knowledge for Feature Ranking. Artificial Intelligence in HCI. Lecture Notes in Computer Science(), vol 14050. S. 98-114. https://doi.org/10.1007/978-3-031-35891-3_7

Lebedeva, A., Kornowicz, J., Lammert, O., Papenkordt, J., (2023) The Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks. Artificial Intelligence in HCI. Lecture Notes in Computer Science(), vol 14050. S. 131-149. https://doi.org/10.1007/978-3-031-35891-3_9. This publication was created in cooperation with Arbeitswelt.Plus (https://arbeitswelt.plus/).