611 citations · 711 across the 9 of their papers we have counts for
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cs.CY2021★ 25 cited
Explainability Auditing for Intelligent Systems: A Rationale for Multi-Disciplinary Perspectives
Markus Langer, Kevin Baum, Kathrin Hartmann +3
National and international guidelines for trustworthy artificial intelligence (AI) consider explainability to be a central facet of trustworthy systems. This paper outlines a multi…
cs.AI2021★ 611 cited
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 +5
Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, a…