{
  "status": "generated_with_standard_library_fallback",
  "missing_optional_dependency": "pandas",
  "install_for_real_fairlearn_metricframe": [
    "python3 -m venv .venv",
    "source .venv/bin/activate",
    "python3 -m pip install pandas fairlearn",
    "python3 ops/audit_with_fairlearn.py --field access_need --write"
  ],
  "field": "access_need",
  "policy": "slice_decision_policy.json",
  "meaning": {
    "auto_recall": "Recall si solo cuenta la decisión automatica de priorizar.",
    "safe_capture": "Recall si cuentan priorizar y revisar, porque ambas evitan flujo normal.",
    "miss_rate": "Casos prioritarios enviados a flujo normal.",
    "priority_selection_rate": "Proporcion de casos priorizados automáticamente."
  },
  "overall": {
    "n": 36,
    "positives": 18,
    "auto_recall": 0.444444,
    "false_positive_rate": 0.055556,
    "priority_selection_rate": 0.25,
    "miss_rate": 0.555556,
    "safe_capture": 0.777778
  },
  "by_group": {
    "no": {
      "n": 24,
      "positives": 12,
      "auto_recall": 0.666667,
      "false_positive_rate": 0.083333,
      "priority_selection_rate": 0.375,
      "miss_rate": 0.333333,
      "safe_capture": 1.0
    },
    "si": {
      "n": 12,
      "positives": 6,
      "auto_recall": 0.0,
      "false_positive_rate": 0.0,
      "priority_selection_rate": 0.0,
      "miss_rate": 1.0,
      "safe_capture": 0.333333
    }
  },
  "difference": {
    "auto_recall": 0.666667,
    "false_positive_rate": 0.083333,
    "priority_selection_rate": 0.375,
    "miss_rate": 0.666667,
    "safe_capture": 0.666667
  },
  "why_fallback_is_kept": "El kit base debe ejecutarse sin dependencias externas. Este fallback permite aprender la lectura por grupos; Fairlearn aporta el MetricFrame profesional cuando está instalado."
}