Clinical AI: opacity, accountability, responsibility and liability
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The aim of this literature review was to compose a narrative review supported by a systematic approach to critically identify and examine concerns about accountability and the allocation of responsibility and legal liability as applied to the clinician and the technologist as applied the use of opaque AI-powered systems in clinical decision making. This review questions (a) if it is permissible for a clinician to use an opaque AI system (AIS) in clinical decision making and (b) if a patient was harmed as a result of using a clinician using an AIS’s suggestion, how would responsibility and legal liability be allocated? Literature was systematically searched, retrieved, and reviewed from nine databases, which also included items from three clinical professional regulators, as well as relevant grey literature from governmental and non-governmental organisations. This literature was subjected to inclusion/exclusion criteria; those items found relevant to this review underwent data extraction. This review found that there are multiple concerns about opacity, accountability, responsibility and liability when considering the stakeholders of technologists and clinicians in the creation and use of AIS in clinical decision making. Accountability is challenged when the AIS used is opaque, and allocation of responsibility is somewhat unclear. Legal analysis would help stakeholders to understand their obligations and prepare should an undesirable scenario of patient harm eventuate when AIS were used.
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Reports have highlighted instances where IBM's Watson for Oncology suggested treatments that contradicted medical guidelines.
“While it aims to provide evidence-based recommendations, reports have highlighted instances where Watson for Oncology suggested treatments that contradicted medical guidelines [141].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [141]
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Abeysiriwardana, P. C., Jayasinghe-Mudalige, U. K., Kodituwakku, S. R., & Madhushani, K. B. (2022) · SN Social Sciences · doi:10.1007/s43545-022-00484-8 · Cited by 10 · see citing works
Generative AI in Terms of Its Ethical Problems for Both Teachers and Learners
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The double-edged sword effects of algorithmic opacity: The self-determination theory perspective
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- Authors
- Smith, H.
- Affiliations
- University of Bristol (GB)
- Corresponding author
- Smith, Helen
- ORCID
- Smith, Helen
- Venue
- AI & SOCIETY
- Published
- 2020-07-25
- Volume
- 36
- Issue
- 2
- Pages
- 535-545
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- OpenAlex
- W3045238219
- Cited by
- 245
- References
- 27
- Citations by year
- 2026: 73 · 2025: 62 · 2024: 53 · 2023: 35 · 2022: 15 · 2021: 6 · 2020: 1
- FWCI
- 4.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI & Society (publishedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- clinical AI, algorithmic opacity, accountability, responsibility allocation, legal liability, clinical decision-making, patient harm, clinicians, technologists, algorithmic transparency
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