Fairness and Health Equity in Machine Learning
Fairness and Health Equity in Machine Learning
This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity.
Topics covered in this lesson
- Fairness and health equity
- Key analytical frameworks
- Power structures and biases inherent in datasets
Documents
Slides
(3.23 MB)
Prerequisites
None
Technology requirement
None
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