Interpretable Machine Learning
The research field is concerned with the development of algorithmic innovations in the area of intrinsically interpretable machine learning models and post-hoc analytical explanation methods for black-box models as well as their evaluation from a technical and socio-technical perspective.
Publications
Machine Learning and Deep Learning
In: Electronic Markets 31 (2021), p. 685–695
ISSN: 1019-6781
DOI: 10.1007/s12525-021-00475-2
URL: https://link.springer.com/article/10.1007/s12525-021-00475-2
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How Much AI Do You Require? Decision Factors for Adopting AI Technology
41st International Conference on Information Systems (ICIS) (Virtual Conference, 13. December 2020 - 16. December 2020)
In: Association for Information Systems (ed.): Proceedings of the 41st International Conference on Information Systems 2020
URL: https://aisel.aisnet.org/icis2020/implement_adopt/implement_adopt/10/
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White, Grey, Black: Effects of XAI Augmentation on the Confidence in AI-based Decision Support Systems
40th International Conference on Information Systems (ICIS) (Virtual Conference, 13. December 2020 - 16. December 2020)
In: Association for Information Systems (ed.): Proceedings of the 40th International Conference on Information Systems 2020
URL: https://aisel.aisnet.org/icis2020/hci_artintel/hci_artintel/14/
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GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
European Conference on Information Systems (Timisoara, 5. July 2022 - 9. July 2022)
In: Proceedings of the 30th European Conference on Information Systems 2022
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Demystifying the Black Box: A Classification Scheme for Interpretation and Visualization of Deep Intelligent Systems
25th Americas Conference on Information Systems (AMCIS) (Cancún, 15. August 2019 - 17. August 2019)
In: Proceedings of the 25th Americas Conference on Information Systems 2019
URL: https://aisel.aisnet.org/amcis2019/ai_semantic_for_intelligent_info_systems/ai_semantic_for_intelligent_info_systems/8/
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