Four Principles of Explainable Artificial Intelligence
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Privacy laws such as GDPR, HIPAA and PIPL all include clauses that state that the handling of healthcare data should be transparent, which means that AI algorithms that work with these data should be transparent and explainable as well.
“Privacy laws such as GDPR, HIPAA and PIPL all include clauses that state that the handling of healthcare data should be transparent, which means that AI algorithms that work with these data should be transparent and explainable as well.”
From Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare (2023) · cites this paper as [21]
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“It is also possible to craft misleading explanations that look trustworthy to the user and that, while they technically reflect the model’s decision process with high accuracy, omit parts of the explanation and ultimately decrease the level of trust in the model [42–44] by allowing vendors to use explainability to “fa…”
BackgroundResultInfluentialDesigning a feature selection method based on explainable artificial intelligence
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Enhancing trust in AI through industry self-governance.
Roski, J., Maier, E. J., Vigilante, K., Kane, E. A., & Matheny, M. E. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab065 · Cited by 83 · see citing works
Hsu, C.-Y., & Li, W. (2023) · International Journal of Geographical Information Science · doi:10.1080/13658816.2023.2191256 · Cited by 75 · see citing works
Darvishvand, L., Kamkari, B., Huang, M. J., & Hewitt, N. J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106492 · Cited by 42 · see citing works
An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering
Mattioli, J., Sohier, H., Delaborde, A., Amokrane-Ferka, K., Awadid, A., Chihani, Z., Khalfaoui, S., & Pedroza, G. (2024) · AI and Ethics · doi:10.1007/s43681-023-00394-2 · Cited by 22 · see citing works
Risk and Exposure of XAI in Persuasion and Argumentation: The case of Manipulation
Carli, R., Najjar, A., & Calvaresi, D. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-15565-9_13 · Cited by 18 · see citing works
Explainable Anomaly Detection for System Logs in Distributed Environments
Gu, Z., Yue, W., & Liu, C. (2026) · Computers, Materials & Continua · doi:10.32604/cmc.2026.077388
“However, current federated learning models face the challenge of insufficient explainability—cybersecurity operations and maintenance personnel find it difficult to understand the detection logic and cannot trace the attack path [12–14].”
- Authors
- Phillips, P. J., Hahn, C. A., Fontana, P. C., Broniatowski, D. A., & Przybocki, M. A.
- Published
- 2020-08-17
- OpenAlex
- W4240187149
- Cited by
- 136
- References
- 91
- Citations by year
- 2026: 7 · 2025: 28 · 2024: 22 · 2023: 33 · 2022: 27 · 2021: 17 · 2020: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Funders
- Information Technology Laboratory
- 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
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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