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Ethical Machine Learning in Healthcare.

Irene Y Chen¹, Emma Pierson², Sherri Rose³

¹Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Annual Review of Biomedical Data Science

|August 16, 2021

Summary

This summary is machine-generated.

Machine learning (ML) in healthcare can worsen health inequities. This study outlines ethical considerations for equitable ML, focusing on social justice and addressing challenges from development to deployment.

Keywords:

bias ethics health health disparities healthcare machine learning

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Area of Science:

Health Informatics
Artificial Intelligence Ethics
Social Determinants of Health

Background:

Machine learning (ML) models in healthcare risk exacerbating existing health disparities.
Ethical frameworks are crucial for ensuring equitable application of ML in medical settings.

Purpose of the Study:

To outline ethical considerations for developing and deploying equitable ML in healthcare.
To frame ML ethics within a social justice perspective.
To identify challenges and propose recommendations for ethical ML implementation.

Main Methods:

Literature review of ethical ML in healthcare.
Analysis of ML development pipelines from a social justice lens.
Synthesis of current efforts and challenges in equitable ML.

Main Results:

Existing ML models may perpetuate or amplify health inequities.
A social justice framework highlights critical ethical junctures in the ML lifecycle.
Challenges exist in problem selection, data representation, model validation, and postdeployment monitoring.

Conclusions:

Addressing ethical concerns in ML for healthcare requires a proactive, social justice-oriented approach.
Recommendations focus on equitable data practices, transparent algorithms, and continuous ethical evaluation.
Achieving equitable ML in health necessitates interdisciplinary collaboration and policy development.