Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges. (15th November 2022)

• Record Type: Journal Article
• Authors: Hatherley, Joshua; Sparrow, Robert
• Is Part Of: Journal of the American Medical Informatics Association. Volume 30:Number 2(2023)
• Page Start: 361
• Page End: 366
• Publication Date: 2022-11-15
• Subjects: artificial intelligence -- bioethics -- update problem -- medicine -- federated learning; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac218
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objectives: Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature. Target audience: The target audiences for this tutorial are the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems.