Work

Ensuring Fairness in Machine Learning to Advance Health Equity

Year: 2018

Type: article

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.

Source: Annals of Internal Medicine

DOI: 10.7326/m18-1990

Authors: Alvin Rajkomar, Michaela Hardt, Michael D. Howell, Greg Corrado, Marshall H. Chin

Institutions: Google (United States), University of California, San Francisco, University of Chicago

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases