Course Description
Technical approaches to fairness in machine learning. Protected attributes and anti-classification. Statistical fairness definitions: demographic parity, equalized odds, equal opportunity, calibration. The impossibility theorems: why you can't satisfy all definitions simultaneously. Group vs. individual fairness. Fairness through unawareness and why it fails. Pre-processing, in-processing, and post-processing mitigation techniques. Fairness in foundation models and LLMs. Lab: audit a real credit scoring model using fairness metrics.