ArticleAI & society2022
"Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations.
Article in AI & society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.BMC medical ethics · 2024Pooled it
- Artificial intelligence and medical futility.BMC medical informatics and decision making · 2026Review
- The Ethics Committee of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI) - Artificial intelligence in end-of-life decision-making processes: ethical reflections.Journal of anesthesia, analgesia and critical care · 2026Article
- An Investigation Into the Comprehension, Perceptions, Application, and Determinants Affecting the Utilization of Deepseek Artificial Intelligence by Healthcare Professionals: A Cross-Sectional Study.Health science reports · 2025Article
- Should We Use Behavioural Predictions in Organ Allocation?Bioethics · 2025Article
- AI ethics for the everyday intensivist.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2025Article
- On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments.BMC medical informatics and decision making · 2025Review
- Machine Learning Operations in Health Care: A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- Digital Transformation of Public Health for Noncommunicable Diseases: Narrative Viewpoint of Challenges and Opportunities.JMIR public health and surveillance · 2024Article
- Should AI allocate livers for transplant? Public attitudes and ethical considerations.BMC medical ethics · 2023Article
- Bioethical challenges in the integration of artificial intelligence in transplant surgery 4.0: A scoping review.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps to maximize patients' benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.