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ai_bias_auditor [2025/05/24 15:54] – [1. Defining Bias Metrics] eagleeyenebulaai_bias_auditor [2025/05/25 03:35] (current) – [AI Bias Auditor] eagleeyenebula
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 The **AI Bias Auditor** is a Python-based framework that identifies and evaluates potential biases in machine learning (ML) models. It provides a structured mechanism to analyze protected features (e.g., gender, race) and their relationship to model performance metrics, such as prediction accuracy. By quantifying fairness gaps and classifying outcomes as biased or unbiased, this tool enables responsible and ethical AI development. The **AI Bias Auditor** is a Python-based framework that identifies and evaluates potential biases in machine learning (ML) models. It provides a structured mechanism to analyze protected features (e.g., gender, race) and their relationship to model performance metrics, such as prediction accuracy. By quantifying fairness gaps and classifying outcomes as biased or unbiased, this tool enables responsible and ethical AI development.
  
 +{{youtube>fruyhZUDY54?large}}
 +
 +-------------------------------------------------------------
 ===== Overview ===== ===== Overview =====
  
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 **Example Bias Report**: **Example Bias Report**:
-```json+<code> 
 +json
 { {
     "gender": {     "gender": {
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     }     }
 } }
-``` +</code>
- +
----+
  
 ===== Implementation Details ===== ===== Implementation Details =====
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 **Class Constructor**: **Class Constructor**:
-```python+<code> 
 +python
 class BiasAuditor: class BiasAuditor:
     def __init__(self, protected_features: List[str], outcome_feature: str):     def __init__(self, protected_features: List[str], outcome_feature: str):
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         :param outcome_feature: Target feature measuring fairness (e.g., accuracy).         :param outcome_feature: Target feature measuring fairness (e.g., accuracy).
         """         """
-```+</code>
  
 **Core Method: evaluate_bias()** **Core Method: evaluate_bias()**
-```python+<code> 
 +python
 def evaluate_bias(self, data: pd.DataFrame) -> dict: def evaluate_bias(self, data: pd.DataFrame) -> dict:
     """     """
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         }         }
     return report     return report
-``` +</code>
- +
----+
  
 ===== Examples ===== ===== Examples =====
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 **Code Example**: **Code Example**:
-```python+<code> 
 +python
 import pandas as pd import pandas as pd
  
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 print("Bias Report:", bias_report) print("Bias Report:", bias_report)
-```+</code>
  
 **Output**: **Output**:
-```+<code>
 Bias Report: { 'gender': { 'group_stats': {'male': 0.873333, 'female': 0.816667}, 'fairness_gap': 0.056666, 'is_biased': False }, 'race': { 'group_stats': {'white': 0.875, 'black': 0.75, 'asian': 0.91}, 'fairness_gap': 0.16, 'is_biased': True } } Bias Report: { 'gender': { 'group_stats': {'male': 0.873333, 'female': 0.816667}, 'fairness_gap': 0.056666, 'is_biased': False }, 'race': { 'group_stats': {'white': 0.875, 'black': 0.75, 'asian': 0.91}, 'fairness_gap': 0.16, 'is_biased': True } }
-```  +</code>
- +
----+
  
 ==== 2. Advanced Example: Custom Bias Threshold ==== ==== 2. Advanced Example: Custom Bias Threshold ====
  
 Modify the bias threshold using a derived class: Modify the bias threshold using a derived class:
-```python+<code> 
 +python
 class CustomBiasAuditor(BiasAuditor): class CustomBiasAuditor(BiasAuditor):
     def __init__(self, protected_features, outcome_feature, bias_threshold=0.1):     def __init__(self, protected_features, outcome_feature, bias_threshold=0.1):
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             details["is_biased"] = details["fairness_gap"] > self.bias_threshold             details["is_biased"] = details["fairness_gap"] > self.bias_threshold
         return report         return report
 +</code>
 # Custom threshold example # Custom threshold example
 +<code>
 auditor = CustomBiasAuditor(protected_features=["gender"], outcome_feature="prediction_accuracy", bias_threshold=0.05) auditor = CustomBiasAuditor(protected_features=["gender"], outcome_feature="prediction_accuracy", bias_threshold=0.05)
 bias_report = auditor.evaluate_bias(data) bias_report = auditor.evaluate_bias(data)
  
 print("Bias Report with Custom Threshold:", bias_report) print("Bias Report with Custom Threshold:", bias_report)
-``` +</code>
- +
----+
  
 ==== 3. Visualizing Bias Using Matplotlib ==== ==== 3. Visualizing Bias Using Matplotlib ====
  
 Add visual insights by plotting group statistics and fairness gaps: Add visual insights by plotting group statistics and fairness gaps:
-```python+<code> 
 +python
 import matplotlib.pyplot as plt import matplotlib.pyplot as plt
  
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 plot_bias_report(bias_report) plot_bias_report(bias_report)
-``` +</code>
- +
----+
  
 ===== Advanced Usage ===== ===== Advanced Usage =====
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 Use a loop to audit multiple datasets efficiently: Use a loop to audit multiple datasets efficiently:
-```python+<code> 
 +python
 data_files = ["dataset1.csv", "dataset2.csv", "dataset3.csv"] data_files = ["dataset1.csv", "dataset2.csv", "dataset3.csv"]
  
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     bias_report = auditor.evaluate_bias(data)     bias_report = auditor.evaluate_bias(data)
     print(f"Bias Report for {file}:\n", bias_report)     print(f"Bias Report for {file}:\n", bias_report)
-``` +</code>
- +
----+
  
 ==== 2. Integration with ML Pipelines ==== ==== 2. Integration with ML Pipelines ====
  
 Audit machine learning models during validation: Audit machine learning models during validation:
-```python+<code> 
 +python
 from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestClassifier
 import pandas as pd import pandas as pd
 +</code>
 # Simulated dataset # Simulated dataset
 +<code>
 data = pd.DataFrame({ data = pd.DataFrame({
     "gender": ["male", "female", "male", "female", "male", "female"],     "gender": ["male", "female", "male", "female", "male", "female"],
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     "loan_approval": [1, 0, 0, 1, 1, 1]     "loan_approval": [1, 0, 0, 1, 1, 1]
 }) })
 +</code>
  
 +<code>
 X = data[["gender", "income", "race"]] X = data[["gender", "income", "race"]]
 y = data["loan_approval"] y = data["loan_approval"]
 +</code>
  
 # Model training # Model training
 +
 +<code>
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
 clf = RandomForestClassifier() clf = RandomForestClassifier()
 clf.fit(pd.get_dummies(X_train), y_train) clf.fit(pd.get_dummies(X_train), y_train)
 +</code>
  
 # Evaluate bias on predictions # Evaluate bias on predictions
 +<code>
 +
 X_test["prediction_accuracy"] = clf.predict(pd.get_dummies(X_test)) X_test["prediction_accuracy"] = clf.predict(pd.get_dummies(X_test))
 auditor = BiasAuditor(protected_features=["gender", "race"], outcome_feature="prediction_accuracy") auditor = BiasAuditor(protected_features=["gender", "race"], outcome_feature="prediction_accuracy")
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 print("Bias Report:", bias_report) print("Bias Report:", bias_report)
-``` +</code>
- +
----+
  
 ===== Applications ===== ===== Applications =====
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 **3. Business Insights**: **3. Business Insights**:
   Detect unintended biases in decision-making systems, such as loan approvals or hiring tools.   Detect unintended biases in decision-making systems, such as loan approvals or hiring tools.
- 
---- 
  
 ===== Best Practices ===== ===== Best Practices =====
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 2. **Custom Thresholds**: Adjust fairness thresholds to fit domain-specific fairness guidelines. 2. **Custom Thresholds**: Adjust fairness thresholds to fit domain-specific fairness guidelines.
 3. **Visualize Results**: Use visualization tools to make bias reports more interpretable. 3. **Visualize Results**: Use visualization tools to make bias reports more interpretable.
- 
---- 
- 
 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Bias Auditor** empowers users to evaluate the fairness of ML models in a structured and interpretable way. Its customizable threshold, extensibility, and integration into ML pipelines make it ideal for building responsible AI systems. By identifying biases early in the development cycle, this tool promotes transparency and accountability in AI. The **AI Bias Auditor** empowers users to evaluate the fairness of ML models in a structured and interpretable way. Its customizable threshold, extensibility, and integration into ML pipelines make it ideal for building responsible AI systems. By identifying biases early in the development cycle, this tool promotes transparency and accountability in AI.
ai_bias_auditor.1748102091.txt.gz · Last modified: 2025/05/24 15:54 by eagleeyenebula