Inherently Interpretable Machine Learning: A Contrasting Paradigm to Post-hoc Explainable AI
How can IIML-based approaches be developed to assess and ensure the compliance of AI systems or ML models with regulatory requirements? Role of Design Science Research for Future DevelopmentsHow can DSR methodologies be applied to develop new interpretable models and IIML-based systems from a broader socio-technical perspective?What are reusable design principles of IIML-based systems?How can action design research contribute to the iterative refinement of IIML-based systems, ensuring their alignment with real-world challenges?
Authors
- Mathias Kraus (ORCID: https://orcid.org/0000-0002-2021-2743)
- Patrick Zschech (ORCID: https://orcid.org/0000-0002-1105-8086)
- Sven Weinzierl (ORCID: https://orcid.org/0000-0003-2268-7352)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Leibniz Institute for Solid State and Materials Research (DE)
- University of Regensburg (DE)
Publication Details
- Journal
- Business & Information Systems Engineering
- Published
- 2025-09-15
- DOI
- https://doi.org/10.1007/s12599-025-00964-0
- Citations
- 2
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 4.96