What Do We Want From Explainable Artificial Intelligence (XAI)? - A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research

By Markus Langer, Daniel Oster, Timo Speith, Holger Hermanns, Lena Kästner, Eva Schmidt, Andreas Sesing, Kevin Baum. Artificial Intelligence, 2021

562 citations recorded by the source corpus.

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these stakeholders' desiderata) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability of artificial systems and reviews their desiderata. We provide 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' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches.

Subfields: Artificial Intelligence, Health Informatics, Safety Research

View Paper: https://www.semanticscholar.org/paper/9dae90ce317bc61eeeeb16a3589fe9201a799de0

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Citations in the corpus

AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap (2023, background): “Within the HCI community, researchers have attempted to guide the development and evaluation of transparency approaches by digging into the reasons why people seek information [109, 176].”
A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods (2022, methodology): “Solely for the year 2021, we are aware of six papers that present some form of taxonomy of explainability methods: [6, 14, 44, 49, 54, 72].”
The Metacognitive Demands and Opportunities of Generative AI (2023, unknown): “Self-ratings of confidence”
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, background): “Exacerbating this issue, using formal verification to guarantee benign behaviour may not be viable due to unverifiability [274].”
Principles and Practice of Explainable Machine Learning (2020, background): “To this end, a series of recent papers address this exact question (van den Berg and Kuiper, 2020; Langer et al., 2021), highlighting the need to consider the point of view of the various stakeholders.”