AI伦理准则：构建值得信赖的智能系统

🎯 偏见类型识别

🛠️ 偏见检测方法

# 偏见检测框架示例
class BiasDetector:
    def __init__(self, model, protected_attributes):
        self.model = model
        self.protected_attributes = protected_attributes
        
    def detect_demographic_parity(self, X, y, sensitive_attr):
        """检测人口统计平等性"""
        # 计算不同群体的正预测率
        groups = X[sensitive_attr].unique()
        positive_rates = {}
        
        for group in groups:
            group_mask = X[sensitive_attr] == group
            group_predictions = self.model.predict(X[group_mask])
            positive_rate = (group_predictions == 1).mean()
            positive_rates[group] = positive_rate
            
        # 计算差异
        max_rate = max(positive_rates.values())
        min_rate = min(positive_rates.values())
        disparity = max_rate - min_rate
        
        return {
            'positive_rates': positive_rates,
            'disparity': disparity,
            'fair': disparity < 0.1  # 10%容忍度
        }
    
    def detect_equalized_odds(self, X, y_true, sensitive_attr):
        """检测机会均等性"""
        y_pred = self.model.predict(X)
        
        metrics = {}
        for group in X[sensitive_attr].unique():
            group_mask = X[sensitive_attr] == group
            
            # 真阳性率和假阳性率
            tpr = self.true_positive_rate(
                y_true[group_mask], 
                y_pred[group_mask]
            )
            fpr = self.false_positive_rate(
                y_true[group_mask], 
                y_pred[group_mask]
            )
            
            metrics[group] = {'TPR': tpr, 'FPR': fpr}
            
        return self.analyze_fairness(metrics)

✅ 偏见缓解策略