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  "Title": "Algorithmic Fairness Assessment for Clinical Prediction Models",
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  "Authors@R": "person(\"Cuiwei\", \"Gao\", , \"48gaocuiwei@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"))",
  "Description": "Post-hoc fairness auditing toolkit for clinical prediction\nmodels. Unlike in-processing approaches that modify model\ntraining, this package evaluates existing models by computing\ngroup-wise fairness metrics (demographic parity, equalized\nodds, predictive parity, calibration disparity), visualizing\ndisparities across protected attributes, and performing\nthreshold-based mitigation. Supports intersectional analysis\nacross multiple attributes and generates audit reports useful\nfor fairness-oriented auditing in clinical AI settings. Methods\ndescribed in Obermeyer et al. (2019)\n<doi:10.1126/science.aax2342> and Hardt, Price, and Srebro\n(2016) <doi:10.48550/arXiv.1610.02413>.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/CuiweiG/clinicalfair,\nhttps://cuiweig.github.io/clinicalfair",
  "BugReports": "https://github.com/CuiweiG/clinicalfair/issues",
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