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
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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).”
From Hidden Risks: Artificial Intelligence and Hermeneutic Harm (2025) · cites this paper as Langer et al., 2021
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“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]
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“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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Cites
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“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
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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-citationExplainable Artificial Intelligence in CyberSecurity: A Survey
Capuano, N., Fenza, G., Loia, V., & Stanzione, C. (2022) · IEEE Access · doi:10.1109/access.2022.3204171 · Cited by 330
“The need to consider the perspectives of 405 diverse stakeholders is highlighted in [34].”
BackgroundThe Metacognitive Demands and Opportunities of Generative AI
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“Self-ratings of confidence”
Moustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3280465 · Cited by 266
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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].”
BackgroundXAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process
Clement, T., Kemmerzell, N., Abdelaal, M., & Amberg, M. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5010006 · Cited by 131
Explainable artificial intelligence for drug discovery and development: a comprehensive survey
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Human-Centered Explainable AI (HCXAI): Beyond Opening the Black-Box of AI
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From Responsibility to Reason-Giving Explainable Artificial Intelligence
Baum, K., Mantel, S., Schmidt, E., & Speith, T. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00510-w · Cited by 103
Self-citationPrinciples and Practice of Explainable Machine Learning
Belle, V., & Papantonis, I. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.688969 · Cited by 99
Survey on Explainable AI: Techniques, challenges and open issues
Abusitta, A., Li, M. Q., & Fung, B. C. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124710 · Cited by 98
AI, Opacity, and Personal Autonomy
Vaassen, B. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00577-5 · Cited by 94
On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives
Sterz, S., Baum, K., Biewer, S., Hermanns, H., Lauber-Rönsberg, A., Meinel, P., & Langer, M. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3659051 · Cited by 91
“Role conflicts could arise if the oversight person is at the same time a decision-maker who uses the AI-based system to support their decisions [43].”
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Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing
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Exploring the effects of human-centered AI explanations on trust and reliance
Scharowski, N., Perrig, S. A. C., Svab, M., Opwis, K., & Brühlmann, F. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1151150 · Cited by 57
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Exploring explainable AI features in the vocal biomarkers of lung disease
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Hidden Risks: Artificial Intelligence and Hermeneutic Harm
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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 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].”
Informing EEG-Based Error Decoding With Explainable AI
Jeremy Chan, H. T., Wimmer, M., Šimić, I., Müller-Putz, G. R., & Veas, E. (2025) · 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc58881.2025.11343575
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
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