Healthcare Resource Allocation, Machine Learning, and Distributive Justice

Abstract

Abstract The literature on the ethics of machine learning in healthcare contains a great deal of work on algorithmic fairness. But a focus on fairness has not been matched with sufficient attention to the relationship between machine learning and distributive justice in healthcare. A significant number of clinical prediction models have been developed which could be used to inform the allocation of scarce healthcare resources. As such, philosophical theories of distributive justice are relevant when considering the ethics of their design and implementation. This paper considers the relationship between machine learning in healthcare and distributive justice with a focus on four aspects of algorithmic design and deployment: the choice of target variable, the model's socio-technical context, the choice of input variables, and the membership of the datasets that models are trained and validated on. Procedural recommendations for how these considerations should be accounted for in the design and implementation of such models follow.

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