Ethical Machine Learning in Health Care

TL;DR

This paper discusses ethical challenges in applying machine learning to health care, emphasizing social justice, and offers recommendations to ensure equitable and responsible deployment of ML models.

Contribution

It provides a comprehensive framework for ethical ML in health care, highlighting challenges and proposing solutions across the entire pipeline from development to deployment.

Findings

Identifies key ethical concerns in health care ML applications

Proposes a social justice framework for ethical considerations

Summarizes actionable recommendations for ethical ML deployment

Abstract

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges.