{
  "_comment": "Example JSON output for a model explanation. This is a template for the model-explainability-tool plugin.",
  "model_id": "model_v3.2",
  "model_type": "Classification",
  "dataset_used": "customer_churn_dataset.csv",
  "explanation_type": "SHAP",
  "explanation_timestamp": "2024-01-26T10:30:00Z",
  "global_explanation": {
    "_comment": "Global feature importance ranking.",
    "feature_importance": [
      {
        "feature": "contract_length",
        "importance_score": 0.35,
        "description": "Length of the customer's contract (e.g., monthly, yearly)."
      },
      {
        "feature": "monthly_charges",
        "importance_score": 0.28,
        "description": "The customer's monthly bill amount."
      },
      {
        "feature": "total_charges",
        "importance_score": 0.22,
        "description": "Total amount the customer has paid."
      },
      {
        "feature": "internet_service",
        "importance_score": 0.10,
        "description": "Type of internet service the customer has (e.g., DSL, Fiber optic)."
      },
      {
        "feature": "online_security",
        "importance_score": 0.05,
        "description": "Whether the customer has online security."
      }
    ],
    "summary": "The model's predictions are most influenced by contract length, monthly charges, and total charges.  Internet service and online security have a smaller, but still significant, impact."
  },
  "local_explanation": {
    "_comment": "Explanation for a specific instance/prediction.",
    "instance_id": "customer_123",
    "predicted_class": "Churn",
    "prediction_probability": 0.85,
    "feature_contributions": [
      {
        "feature": "contract_length",
        "contribution": -0.40,
        "value": "Month-to-month",
        "reason": "Month-to-month contracts are highly correlated with churn."
      },
      {
        "feature": "monthly_charges",
        "contribution": 0.25,
        "value": 75.50,
        "reason": "Higher monthly charges increase the likelihood of churn."
      },
      {
        "feature": "total_charges",
        "contribution": -0.10,
        "value": 200.00,
        "reason": "Relatively low total charges suggest the customer is new and more likely to churn."
      },
      {
        "feature": "internet_service",
        "contribution": 0.05,
        "value": "Fiber optic",
        "reason": "Fiber optic service is associated with higher churn rates in this dataset."
      },
      {
        "feature": "online_security",
        "contribution": -0.02,
        "value": "No",
        "reason": "Lack of online security slightly increases churn risk."
      }
    ],
    "summary": "This customer is predicted to churn primarily due to their month-to-month contract and high monthly charges. The relatively low total charges also contribute to the prediction."
  },
  "fairness_metrics": {
    "_comment": "Metrics for assessing fairness across different groups.",
    "protected_attribute": "gender",
    "metric": "Disparate Impact",
    "value": 0.95,
    "threshold": 0.8,
    "status": "Acceptable",
    "summary": "The model exhibits acceptable disparate impact across genders, as the value (0.95) is above the threshold (0.8)."
  },
  "data_bias_detection": {
    "_comment": "Results of data bias detection.",
    "potential_bias": "Unequal representation of geographic regions in the training data.",
    "recommendations": [
      "Collect more data from underrepresented regions.",
      "Use re-weighting techniques to balance the data."
    ]
  }
}