Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges

Leveraging this'adaptive' potential of medical ML could generate significant benefits for patient health and well-being. Recent engagements with the ethical issues generated by the use of adaptive ML systems in medicine have typically been limited to discussions of'the update problem': how should systems that continue to change and evolve post-regulatory approval be regulated? In this paper, we draw attention to an important set of ethical issues raised by the use of adaptive machine learning systems in medicine that have, thus far, been neglected and are highly deserving of further attention. Discussions of adaptive machine learning systems to date have overlooked the distinction between two sorts of variance that such systems may exhibit -- diachronic evolution (change over time) and synchronic variation (difference between cotempo-raneous instantiations of the algorithmic system at different sites) -- and underestimated the significance of the latter. Both diachronic evolution and synchronic variation will complicate the hermeneutic task of clinicians in interpreting the outputs of AI systems, and will therefore pose significant challenges to the process of securing informed consent to treatment. Equity issues may occur where synchronic variation is permitted, as the quality of care may vary significantly across patients or between hospitals. However, the decision as to whether to allow or eliminate synchronic variation involves complex trade-offs between accuracy and generalisability, as well as a number of other values, including justice and non-maleficence. In some contexts, preventing synchronic variation from emerging may only be possible at the expense of the wellbeing, and the quality of care available to, particular patients or classes of patients. Designers and regulators of adaptive ML systems will need to confront these issues if the potential benefits of adaptive ML in medical care are to be realised.