Ethical Machine Learning in Healthcare

Abstract

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare 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 postdeployment considerations. We close by summarizing recommendations to address these challenges.

Keywords: bias; ethics; health; health disparities; healthcare; machine learning.

Figure 1

We motivate the five steps in the ethical pipeline for healthcare model development. Each stage contains considerations for machine learning where ignoring technical challenges violate the bioethical principle of justice, either by exacerbating existing social injustices or by creating the potential for new injustices between groups. Although this review’s ethical focus is on social justice, the challenges that we highlight may also violate ethical principles such as justice and beneficence. We highlight a few in this illustration.

Figure 2

The model development pipeline contains many challenges for ethical machine learning for healthcare. We highlight both visible and hidden challenges.