Project B01: A dialog-based approach to explaining machine learning models
In Project B01, researchers are working on an Artificial Intelligence (AI) based system that can properly respond to questions at the level of language. In medicine, for example, the system should be able to explain a proposed treatment to a doctor and respond to patients’ questions and concerns regarding their treatment plan. The computer scientists and sociologists working on this project are including users’ perspectives in their research. For this, they are observing how, for instance, healthcare workers adopt this AI system and what requirements they have of the system. Based on these results, the researchers are developing a dialog system that can be used in a number of different areas of society.
Research areas: Computer science, Sociology
Associate member
Dr. Stefan Heindorf, Paderborn University
Support staff
Hakki Egemen Gülpinar, Bielefeld University
Publications
User Involvement in Training Smart Home Agents
L.N. Sieger, J. Hermann, A. Schomäcker, S. Heindorf, C. Meske, C.-C. Hey, A. Doğangün, in: International Conference on Human-Agent Interaction, ACM, 2022.
Explaining Machines: Social Management of Incomprehensible Algorithms. Introduction
E. Esposito, Sociologica 16 (2023) 1–4.
Dung's Argumentation Framework: Unveiling the Expressive Power with Inconsistent Databases
Y. Mahmood, M. Hecher, A.-C. Ngonga Ngomo, in: 2024.
Rejection in Abstract Argumentation: Harder Than Acceptance?
J.K. Fichte, M. Hecher, Y. Mahmood, A. Meier, in: Frontiers in Artificial Intelligence and Applications, IOS Press, 2024.
Quantitative Claim-Centric Reasoning in Logic-Based Argumentation
M. Hecher, Y. Mahmood, A. Meier, J. Schmidt, in: Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, 2024.
Parameterised Complexity of Consistent Query Answering via Graph Representations
T. Hankala, M. Hannula, Y. Mahmood, A. Meier, ArXiv:2412.08324 (2024).
Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues
L. Fichtel, M. Spliethöver, E. Hüllermeier, P. Jimenez, N. Klowait, S. Kopp, A.-C. Ngonga Ngomo, A. Robrecht, I. Scharlau, L. Terfloth, A.-L. Vollmer, H. Wachsmuth, ArXiv:2504.18483 (2025).
Dung’s Argumentation Framework: Unveiling the Expressive Power with Inconsistent Databases
Y. Mahmood, M. Hecher, A.-C. Ngonga Ngomo, Proceedings of the AAAI Conference on Artificial Intelligence 39 (2025) 15058–15066.
Logics with probabilistic team semantics and the Boolean negation
M. Hannula, M. Hirvonen, J. Kontinen, Y. Mahmood, A. Meier, J. Virtema, Journal of Logic and Computation 35 (2025).
Facets in Argumentation: A Formal Approach to Argument Significance
J. Fichte, N. Fröhlich, M. Hecher, V. Lagerkvist, Y. Mahmood, A. Meier, J. Persson, ArXiv:2505.10982 (2025).
Why not? Developing ABox Abduction beyond Repairs
A.P.H. Haak, P. Koopmann, Y. Mahmood, A.-Y. Turhan, ArXiv:2507.21955 (2025).
Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues
L. Fichtel, M. Spliethöver, E. Hüllermeier, P. Jimenez, N. Klowait, S. Kopp, A.-C. Ngonga Ngomo, A. Robrecht, I. Scharlau, L. Terfloth, A.-L. Vollmer, H. Wachsmuth, in: Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Association for Computational Linguistics, Avignon, France, n.d.
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