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  "Title": "Machine Learning Prediction Auditing and Model Card Reporting",
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  "Description": "Audits predictions from fitted machine learning models\nwithout requiring retraining or access to the fitted model\nobject. It supports binary classification, multiclass\nclassification, and regression, providing performance metrics\nwith bootstrap confidence intervals, calibration and threshold\nanalyses, residual diagnostics, subgroup comparisons,\nuser-defined acceptance criteria, risk warnings, and\nself-contained reports. For binary-classification and\nregression models, the package can also assemble and render\nstructured model cards documenting intended use, evaluation\ndata, performance, subgroup results, assumptions, and\nlimitations. The model-card reporting approach is described by\nMitchell et al. (2019) \"Model Cards for Model Reporting\"\n<doi:10.1145/3287560.3287596>.",
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  "Author": "Georgios P. Georgiou [aut, cre]",
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    "add_limitations",
    "add_purpose",
    "audit_assumptions",
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    "audit_predictions",
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      "title": "Add intended-use information",
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      "title": "Run model-specific diagnostic checks",
      "topics": [
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    {
      "page": "audit_fairness",
      "title": "Audit subgroup performance and fairness",
      "topics": [
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      "page": "audit_performance",
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      "title": "Audit Predictions from a Completed Model",
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      "title": "Define prediction-audit requirements",
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      "page": "compare_cards",
      "title": "Compare two model cards",
      "topics": [
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    {
      "page": "compare_predictions",
      "title": "Compare two sets of binary-classification predictions",
      "topics": [
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      "page": "model_card",
      "title": "Create a model card",
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      "title": "Compatibility Interface for Prediction Audits",
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      "title": "Plot a prediction audit",
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