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

Project leaders

Prof. Dr. Eyke Hüllermeier

Transregional Collaborative Research Centre 318

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Prof. Dr. Kirsten Thommes

Transregional Collaborative Research Centre 318

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Staff

Stefan Heid, M.Sc.

Transregional Collaborative Research Centre 318

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Jaroslaw Kornowicz, M.Sc.

Transregional Collaborative Research Centre 318

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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).


Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain

J. Hanselle, J. Kornowicz, S. Heid, K. Thommes, E. Hüllermeier, in: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings, 2023.


Probabilistic Scoring Lists for Interpretable Machine Learning

J.M. Hanselle, J. Fürnkranz, E. Hüllermeier, in: Discovery Science, Springer Nature Switzerland, Cham, 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).


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