Ethics of AI and Health Care: Towards a Substantive Human Rights Framework
Open accessAbstract
There is enormous interest in using artificial intelligence (AI) in health care contexts. But before AI can be used in such settings, we need to make sure that AI researchers and organizations follow appropriate ethical frameworks and guidelines when developing these technologies. In recent years, a great number of ethical frameworks for AI have been proposed. However, these frameworks have tended to be abstract and not explain what grounds and justifies their recommendations and how one should use these recommendations in practice. In this paper, I propose an AI ethics framework that is grounded in substantive, human rights theory and one that can help us address these questions.
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Some of these frameworks provide practical checklists that organizations can use to conduct an ethics deliberation.
“Some of these frameworks provide practical checklists that organizations can use to conduct an ethics deliberation.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [94]
Section 4 says the paper’s Fundamental Conditions Approach helps healthcare organizations identify and justify ethical considerations, which supports its use in ethical deliberation. But our copy does not describe it or other frameworks as providing practical checklists; the Introduction instead says most existing frameworks offer little concrete guidance. Page numbers are unavailable in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredBuolamwini and Gebru and Liao note ethical concerns around algorithms not detecting dark-skinned individuals for skin cancer detection due to the algorithm being trained on light-skinned individuals.
“Buolamwini and Gebru [85] and Liao [94] note ethical concerns around algorithms not detecting dark-skinned individuals for skin cancer detection due to the fact that the algorithm was trained on light-skinned individuals.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [94]
Section 2, “Machine Learning: Key Concepts and Current Limitations,” raises concern that skin-cancer algorithms may perform worse for people with darker skin because many are trained primarily on light-skinned individuals. The citing sentence overstates this as established failure to detect skin cancer, rather than a concern about potentially poorer detection. (LLM, conf=0.87)
Experimental — accuracy not yet measuredAccording to Liao, there are >80 ethical frameworks that have been proposed for AI.
“According to Liao [94], there are >80 ethical frameworks that have been proposed for AI.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [94]
Liao provides an example of a prediction of a 70% chance for a supposed patient's tumor to become malignant in 5 years; however, the algorithm does not necessarily provide detailed reasons as to how it arrived at the conclusion.
“They provide an example of a prediction of a 70% chance for a supposed patient’s tumor to become malignant in 5 years; however, the algorithm does not necessarily provide detailed reasons as to how it arrived at the conclusion.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [94]
Many of these frameworks draw on the 4 principles of biomedical ethics, namely, autonomy, beneficence, nonmaleficence, and justice.
“Many of these draw on the 4 principles of biomedical ethics, namely, autonomy, beneficence, nonmaleficence, and justice.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [94]
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Cited by
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- Authors
- Liao, S. M.
- Affiliations
- New York University (US)
- Corresponding author
- Liao, S. Matthew
- ORCID
- Liao, S. Matthew
- Venue
- Topoi
- Published
- 2023-04-12
- Volume
- 42
- Issue
- 3
- Pages
- 857-866
- ISSN
- 0167-7411 (ISSN-L)
- 1572-8749
- OpenAlex
- W4365148516
- Cited by
- 17
- References
- 28
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 6 · 2023: 1
- FWCI
- 4.26
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-05-02)
- Open access
- hybrid
- doi.org
- Locations
- Topoi (publishedVersion, cc-by)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- AI ethics, health care, human rights, AI ethics framework
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