Lena Kästner
Saarland University
7 Papers
2 Citations
Lena Kästner is an academic researcher from Saarland University. The author has contributed to research in topics: Computer science & Conceptual model (computer science). The author has an hindex of 2, co-authored 7 publications.
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Papers
What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
Markus Langer,Daniel Oster,Timo Speith,Holger Hermanns,Lena Kästner,Eva Schmidt,Andreas Sesing,Kevin Baum +7 more
TL;DR: In this paper, the main classes of stakeholders calling for explainability of artificial systems and reviews their desiderata are discussed and a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desidersata.
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The Enigmatic Metallothioneins: A Case of Upward-Looking Research.
TL;DR: This contribution sets out to show how philosophy of science can help to gain a clearer picture of biochemical research and how exploratory and upward-looking research play important roles in biochemical discoveries although they do not fit the paradigmatic approach of decomposition and struc-ture-function mapping.
Disambiguating "Mechanisms" in Pharmacy: Lessons from Mechanist Philosophy of Science.
TL;DR: It is argued that while pharmacokinetic mechanisms usually describe causal chains of production, pharmacodynamics tends to focus on mechanisms of action underlying the in vivo effects of a drug, and the resulting Baumkuchen model provides a powerful and practical alternative to traditional flat schemes.
The New Mechanical Philosophy: by Stuart Glennan, Oxford, Oxford University Press, 2017, xii + 266 pp., ISBN 9780198779711, £30.00, US$40.95 (hardback)
TL;DR: Stuart Glennan's early work on mechanisms (Glennan 1996, 2002) pre-dates seminal publications on mechanisms and mechanistic explanations in philosophy of science (e.g. Machamer, Darden, and Craver...).
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On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness.
TL;DR: In this article, the authors argue that even though trustworthiness does not automatically lead to trust, there are several reasons to engineer primarily for trustworthiness and that a system's explainability can crucially contribute to its trustworthiness.