Fairness

Evaluating a machine learning model (ML) responsibly requires doing more than just calculating overall loss metrics. Before putting a model into production, it's critical to audit training data and evaluate predictions for bias.

This module looks at different types of human biases that can manifest in training data. It then provides strategies to identify and mitigate them, and then evaluate model performance with fairness in mind.