38：永不疲劳主观偏见解决：AI vs 人类决策对比实验

class BiasDetector:
    def __init__(self):
        self.bias_metrics = {
            'gender_bias': 0,
            'race_bias': 0,
            'age_bias': 0,
            'socioeconomic_bias': 0
        }
    
    def detect_bias(self, decision_data):
        """检测决策中的偏见"""
        # 分析决策数据中的偏见
        for metric in self.bias_metrics:
            self.bias_metrics[metric] = self._calculate_bias(decision_data, metric)
        
        return self.bias_metrics
    
    def eliminate_bias(self, decision_model, training_data):
        """消除模型中的偏见"""
        # 生成去偏见的训练数据
        debiased_data = self._generate_debiased_data(training_data)
        
        # 重新训练模型
        debiased_model = self._retrain_model(decision_model, debiased_data)
        
        return debiased_model
    
    def _calculate_bias(self, decision_data, metric):
        """计算特定维度的偏见"""
        # 实现偏见计算逻辑
        pass
    
    def _generate_debiased_data(self, training_data):
        """生成去偏见的训练数据"""
        # 实现数据去偏见逻辑
        pass
    
    def _retrain_model(self, decision_model, debiased_data):
        """用去偏见的数据重新训练模型"""
        # 实现模型重训练逻辑
        pass

class ContinuousLearner:
    def __init__(self, initial_model):
        self.model = initial_model
        self.feedback_history = []
    
    def learn_from_feedback(self, decision, feedback):
        """从反馈中学习"""
        # 记录反馈
        self.feedback_history.append((decision, feedback))
        
        # 当反馈积累到一定数量时，更新模型
        if len(self.feedback_history) >= 100:
            self._update_model()
    
    def _update_model(self):
        """更新模型"""
        # 从反馈历史中提取训练数据
        training_data = self._extract_training_data()
        
        # 微调模型
        self.model = self._fine_tune_model(training_data)
        
        # 清空反馈历史
        self.feedback_history = []
    
    def _extract_training_data(self):
        """从反馈历史中提取训练数据"""
        # 实现数据提取逻辑
        pass
    
    def _fine_tune_model(self, training_data):
        """微调模型"""
        # 实现模型微调逻辑
        pass

class DecisionExplainer:
    def __init__(self, decision_model):
        self.model = decision_model
    
    def explain_decision(self, input_data):
        """解释决策过程"""
        # 获取模型的决策结果
        decision = self.model.predict(input_data)
        
        # 分析决策过程
        feature_importance = self._calculate_feature_importance(input_data)
        decision_path = self._extract_decision_path(input_data)
        confidence = self._calculate_confidence(input_data)
        
        # 生成解释
        explanation = {
            'decision': decision,
            'feature_importance': feature_importance,
            'decision_path': decision_path,
            'confidence': confidence
        }
        
        return explanation
    
    def _calculate_feature_importance(self, input_data):
        """计算特征重要性"""
        # 实现特征重要性计算
        pass
    
    def _extract_decision_path(self, input_data):
        """提取决策路径"""
        # 实现决策路径提取
        pass
    
    def _calculate_confidence(self, input_data):
        """计算决策置信度"""
        # 实现置信度计算
        pass

class AIDecisionConfig:
    def __init__(self):
        self.model_type = 'ensemble'
        self.bias_detection = True
        self.continuous_learning = True
        self.explainability = True
        self.confidence_threshold = 0.8
    
    def get_config(self):
        return {
            'model_type': self.model_type,
            'bias_detection': self.bias_detection,
            'continuous_learning': self.continuous_learning,
            'explainability': self.explainability,
            'confidence_threshold': self.confidence_threshold
        }