Example_AI_Fairness_360_to_audit_a_hypothetical_model.ipynb

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      "authorship_tag": "ABX9TyN+RdfQAL6aS23/kGg7JqaT",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/OneFineStarstuff/OneFineStardust/blob/main/Example_AI_Fairness_360_to_audit_a_hypothetical_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install aif360[Reductions]"
      ],
      "metadata": {
        "id": "VFcSQ9mJ1eIG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install aif360 scikit-learn"
      ],
      "metadata": {
        "id": "xODf7di_2Jgb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "AroLgI-N0tV0"
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      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from aif360.datasets import StandardDataset\n",
        "from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric\n",
        "from aif360.algorithms.preprocessing import Reweighing\n",
        "from aif360.algorithms.postprocessing import CalibratedEqOddsPostprocessing\n",
        "\n",
        "# Hypothetical function to load your dataset\n",
        "def load_your_data():\n",
        "    # Replace this with your actual data loading logic\n",
        "    import pandas as pd\n",
        "    data = pd.DataFrame({\n",
        "        'feature1': [0.1, 0.2, 0.3, 0.4],\n",
        "        'feature2': [1, 2, 3, 4],\n",
        "        'label': [0, 1, 1, 0],\n",
        "        'attribute': [1, 0, 1, 0]\n",
        "    })\n",
        "    return data\n",
        "\n",
        "# Load your dataset\n",
        "data = load_your_data()\n",
        "\n",
        "# Convert to AIF360 dataset\n",
        "dataset = StandardDataset(df=data, label_name='label', favorable_classes=[1], protected_attribute_names=['attribute'], privileged_classes=[[1]])\n",
        "\n",
        "# Initialize and fit the model\n",
        "model = RandomForestClassifier()\n",
        "model.fit(dataset.features, dataset.labels.ravel())\n",
        "\n",
        "# Make predictions\n",
        "predictions = model.predict(dataset.features)\n",
        "\n",
        "# Create a binary label dataset for metrics\n",
        "predicted_dataset = dataset.copy()\n",
        "predicted_dataset.labels = predictions\n",
        "\n",
        "# Calculate fairness metrics\n",
        "metric = ClassificationMetric(dataset, predicted_dataset, unprivileged_groups=[{'attribute': 0}], privileged_groups=[{'attribute': 1}])\n",
        "print(\"Disparate Impact Ratio:\", metric.disparate_impact())\n",
        "print(\"Average Odds Difference:\", metric.average_odds_difference())\n",
        "\n",
        "# Post-processing: Adjust thresholds to mitigate bias\n",
        "postprocessor = CalibratedEqOddsPostprocessing(privileged_groups=[{'attribute': 1}], unprivileged_groups=[{'attribute': 0}])\n",
        "postprocessor = postprocessor.fit(dataset, predicted_dataset)\n",
        "audited_predictions = postprocessor.predict(predicted_dataset)\n",
        "\n",
        "# Generate audit report\n",
        "audit_report = {\n",
        "    \"Disparate Impact Ratio\": metric.disparate_impact(),\n",
        "    \"Average Odds Difference\": metric.average_odds_difference(),\n",
        "    \"Audited Predictions\": audited_predictions.labels\n",
        "}\n",
        "\n",
        "print(\"Compliance Report:\", audit_report)"
      ]
    }
  ]
}