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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; 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).”
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“All options for enhancing system understandability aim at improving epistemic access [38, 48].”
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“In the XAI literature, this action-enabling potential of transparent decision algorithms often goes unmentioned.”
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Flagged first, then by citations
Research works printed but not linked: 1
- Lipton, 2018, The mythos of model interpretability, Commun. ACM, 61, 36, 10.1145/3233231 · DOI · Paper page
Cited by
Sorted by citations
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
Grassini, S. (2023) · Education Sciences · doi:10.3390/educsci13070692 · Cited by 1,048
Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983
A., S., & R., S. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100230 · Cited by 432
Langer, M., & Landers, R. N. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106878 · Cited by 338
Self-citation
Metadata
- 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
- 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)
- Physical Sciences › Computer Science › Artificial Intelligence › 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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