The DOI matches its Crossref record; 240 of the 240 references it cites were found in a registry or an index.
What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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 citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Causality : Models , Reasoning , and Inference
Bentler, P., Heslenfeld, D., Molenaar, P., & Mcdonald, R. (2002) · Choice Reviews Online · doi:10.5860/choice.47-3771 · Cited by 13,039
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · cited 2×
“This can again decrease the effectiveness of the human-system interaction, as humans rely on the system’s outputs even in situations where they should challenge them [117, 121].”
BackgroundPeeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
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 ›
Are you an author, or do you see a problem with this page? Request a review
