Ensuring Fairness in Machine Learning to Advance Health Equity

Abstract

Machine learning is used increasingly in clinical care to improve diagnosis, treatment selection, and health system efficiency. Because machine-learning models learn from historically collected data, populations that have experienced human and structural biases in the past-called protected groups-are vulnerable to harm by incorrect predictions or withholding of resources. This article describes how model design, biases in data, and the interactions of model predictions with clinicians and patients may exacerbate health care disparities. Rather than simply guarding against these harms passively, machine-learning systems should be used proactively to advance health equity. For that goal to be achieved, principles of distributive justice must be incorporated into model design, deployment, and evaluation. The article describes several technical implementations of distributive justice-specifically those that ensure equality in patient outcomes, performance, and resource allocation-and guides clinicians as to when they should prioritize each principle. Machine learning is providing increasingly sophisticated decision support and population-level monitoring, and it should encode principles of justice to ensure that models benefit all patients.

Figure. Conceptual framework of how various biases relate to one another.

During model development, differences in the distribution of features used to predict a label between the protected and nonprotected groups may bias a model to be less accurate for protected groups. Moreover, the data used to develop a model may not generalize to the data used during model deployment (training–serving skew). Biases in model design and data affect patient outcomes through the model’s interaction with clinicians and patients.