Algorithmic Fairness Assessment for Clinical Prediction Models // Post-hoc fairness auditing toolkit for clinical prediction models. Unlike in-processing approaches that modify model training, this package evaluates existing models by computing group-wise fairness metrics (demographic parity, equalized odds, predictive parity, calibration disparity), visualizing disparities across protected attributes, and performing threshold-based mitigation. Supports intersectional analysis across multiple attributes and generates audit reports useful for fairness-oriented auditing in clinical AI settings. Methods described in Obermeyer et al. (2019) doi:10.1126/science.aax2342 and Hardt, Price, and Srebro (2016) doi:10.48550/arXiv.1610.02413.