Inherently interpretable machine learning: A contrasting paradigm to post-hoc explainable AI (Forthcoming)

Zschech P, Weinzierl S, Kraus M (2025)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2025

Journal

Business & Information Systems Engineering Springer Verlag (Germany)

Pages Range: 1 - 19

URI: https://link.springer.com/article/10.1007/s12599-025-00964-0

DOI: https://doi.org/10.1007/s12599-025-00964-0

Authors with CRIS profile

Patrick Zschech
Sven Weinzierl Lehrstuhl für Digital Industrial Service Systems

Involved external institutions

Technische Universität Dresden DE Germany (DE) Universität Regensburg DE Germany (DE)

How to cite

APA:

Zschech, P., Weinzierl, S., & Kraus, M. (2025). Inherently interpretable machine learning: A contrasting paradigm to post-hoc explainable AI (Forthcoming). Business & Information Systems Engineering, 1 - 19. https://doi.org/https://doi.org/10.1007/s12599-025-00964-0

MLA:

Zschech, Patrick, Sven Weinzierl, and Mathias Kraus. "Inherently interpretable machine learning: A contrasting paradigm to post-hoc explainable AI (Forthcoming)." Business & Information Systems Engineering (2025): 1 - 19.

BibTeX: Download