What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
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The requirements of a developer are not those of an end-user; among end-users, the needs of a job or loan applicant are not necessarily those of a scientist, healthcare professional, judge, or intelligence analyst.
“The requirements of a developer are not those of an end-user; and among end-users, the needs of a job or loan applicant are not necessarily those of, say, a scientist, healthcare professional, judge, or intelligence analyst (Gunning et al., 2019;Langer et al., 2021).”
From Hidden Risks: Artificial Intelligence and Hermeneutic Harm (2025) · cites this paper as Langer et al., 2021
All options for enhancing system understandability aim at improving epistemic access.
“All options for enhancing system understandability aim at improving epistemic access [38, 48].”
From On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives (2024) · cites this paper as [48]
In the XAI literature, the action-enabling potential of transparent decision algorithms often goes unmentioned.
“In the XAI literature, this action-enabling potential of transparent decision algorithms often goes unmentioned.”
From AI, Opacity, and Personal Autonomy (2022) · cites this paper as Langer et al., 2021
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Flagged first, then by citations
Research works printed but not linked: 11
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“The need to consider the perspectives of 405 diverse stakeholders is highlighted in [34].”
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“We aimed to capture the various disciplinary perspectives on explainability, ranging from philosophy, to medicine, to computer science, and law [14,17,18,59,60].”
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Rebera, A. P., Lauwaert, L., & Oimann, A.-K. (2025) · Minds and Machines · doi:10.1007/s11023-025-09733-0 · Cited by 6 · see citing works
“The requirements of a developer are not those of an end-user; and among end-users, the needs of a job or loan applicant are not necessarily those of, say, a scientist, healthcare professional, judge, or intel - ligence analyst (Gunning et al., 2019 ; Langer et al., 2021 ).”
Mohamed, A. S., Fritz, C., Saber, A. M., Ma, Y., Kaur, R., Al-Darwbi, M., Friedrich, D., & Kundur, D. (2026) · arXiv · doi:10.48550/arxiv.2609.00171
“The effectiveness of an explanation is not solely a function of its quality, but also of the user’s cognitive capacity, domain knowledge, alertness, and available time [57], [130].”
- Authors
- Langer, M., Oster, D., Speith, T., Hermanns, H., Kästner, L., Schmidt, E., Sesing, A., & Baum, K.
- Affiliations
- Saarland University (DE)
- TU Dortmund University (DE)
- Corresponding author
- Langer, Markus
- Speith, Timo
- ORCID
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 296
- Pages
- 103473
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W3131457744
- Cited by
- 652
- References
- 240
- Citations by year
- 2026: 131 · 2025: 157 · 2024: 147 · 2023: 119 · 2022: 67 · 2021: 30 · 2020: 1
- FWCI
- 30.57
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1461 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
- ERef Bayreuth (University of Bayreuth) (submittedVersion)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- arXiv (Cornell University)
- Publications of the UdS (Saarland University)
- Funders
- Deutsche Forschungsgemeinschaft (389792660, TRR 248)
- European Research Council (695614)
- Volkswagen Foundation (AZ 98512, AZ 98513, AZ 98509, AZ 98511, AZ 95143, AZ 98514, AZ 98510)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- explainable AI, stakeholders, conceptual model, interdisciplinary research
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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