Ethical Machine Learning in Health Care
TL
TL;DR
Ethics of ML in healthcare is frame through the lens of social justice, and ongoing efforts and challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations are described.
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.
References156 items
1
Thalidomide in America
2
Fairness, Equality, and Power in Algorithmic Decision-Making
3
Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration
4
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data
5