当“算法”倾听心灵：AI心理咨询机器人（如Woebot）的伦理边界与代码实战

用户文本 → 意图识别（自杀/焦虑/闲聊） → 策略选择（CBT/正念/转人工） → 生成回复 → 情绪追踪（PHQ-9/GAD-7）

from sklearn.datasets import make_classification
import pandas as pd
X, y = make_classification(n_samples=20_000, n_features=768, n_classes=2,
                           weights=[0.9,0.1], random_state=42)
demo = pd.DataFrame(X)
demo['label'] = y
demo['race'] = demo.index % 3   # 0=白人 1=黑人 2=拉美

import torch, torch.nn as nn
from opacus import GradSampleModule  # 差分隐私
class FairRoBERTa(nn.Module):
    def __init__(self, n_classes=2):
        super().__init__()
        self.roberta = torch.hub.load('pytorch/fairseq',
                                      'roberta.base', torch_dtype='float16')
        self.classifier = nn.Linear(768, n_classes)
        self.adversary = nn.Linear(768, 3)  # 预测race，对抗训练
    def forward(self, input_ids, race=None, alpha=1.0):
        x = self.roberta.extract_features(input_ids).mean(1)
        y_logit = self.classifier(x)
        if self.training and race is not None:
            race_logit = self.adversary(x)
            return y_logit, race_logit
        return y_logit

from flwr.client import NumPyClient
class FlowerClient(NumPyClient):
    def fit(self, parameters, config):
        net.train()
        # 加载本地数据
        for epoch in range(3):
            for batch in local_loader:
                opt.zero_grad()
                y_logit, race_logit = net(batch['input_ids'],
                                          batch['race'], alpha=0.8)
                cls_loss = F.cross_entropy(y_logit, batch['label'])
                adv_loss = F.cross_entropy(race_logit, batch['race'])
                loss = cls_loss - adv_loss  # 对抗，去掉种族信号
                loss.backward()
                opt.step()
        return self.get_parameters(), len(trainset), {}

def ethical_reply(user_text, risk_model, threshold=0.35):
    prob = risk_model(user_text).sigmoid().item()
    if prob > threshold:
        return ("[高危] 已为你接通真人咨询师，"
                "全国24h热线 400-821-1215"), True
    else:
        return generate_cbt_response(user_text), False