Generalized Omni-dimensional Development

User Tools

Site Tools

Trace:
ai_bias_auditor

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision		Previous revision
ai_bias_auditor [2025/05/24 16:02] – [1. Basic Bias Analysis] eagleeyenebulaai_bias_auditor [2025/05/25 03:35] (current) – [AI Bias Auditor] eagleeyenebula
Line 3: Line 3:
 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 =====
  
Line 134: Line 137:
  
 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):
Line 145: Line 149:
             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
  
Line 176: Line 180:
  
 plot_bias_report(bias_report) plot_bias_report(bias_report)
-``` +</code>
- +
----+
  
 ===== Advanced Usage ===== ===== Advanced Usage =====
Line 185: Line 187:
  
 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"]
  
Line 193: Line 196:
     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"],
Line 212: Line 215:
     "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")
Line 227: Line 238:
  
 print("Bias Report:", bias_report) print("Bias Report:", bias_report)
-``` +</code>
- +
----+
  
 ===== Applications ===== ===== Applications =====
Line 241: Line 250:
 **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 =====
Line 249: Line 256:
 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.1748102562.txt.gz · Last modified: 2025/05/24 16:02 by eagleeyenebula