What makes clinical machine learning fair? A practical ethics framework

Abstract

Machine learning (ML) can offer a tremendous contribution to medicine by streamlining decision-making, reducing mistakes, improving clinical accuracy and ensuring better patient outcomes. The prospects of a widespread and rapid integration of machine learning in clinical workflow have attracted considerable attention including due to complex ethical implications-algorithmic bias being among the most frequently discussed ML models. Here we introduce and discuss a practical ethics framework inductively-generated via normative analysis of the practical challenges in developing an actual clinical ML model (see case study). The framework is usable to identify, measure and address bias in clinical machine learning models, thus improving fairness as to both model performance and health outcomes. We detail a proportionate approach to ML bias by defining the demands of fair ML in light of what is ethically justifiable and, at the same time, technically feasible in light of inevitable trade-offs. Our framework enables ethically robust and transparent decision-making both in the design and the context-dependent aspects of ML bias mitigation, thus improving accountability for both developers and clinical users.

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