Lecture
Fairness in Machine Learning
Description
This lecture covers the sources of unfairness in a machine learning pipeline, the importance of fairness metrics, and the evaluation of model predictions using various fairness metrics. It discusses the concepts of fairness through blindness, awareness, and different fairness metrics like demographic parity, equalized odds, and predictive value parity.
Official source
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