EthicMind：多轮对话中面向伦理-情感对齐的风险感知框架
EthicMind: A Risk-Aware Framework for Ethical-Emotional Alignment in Multi-Turn Dialogue
AI总结：
本文提出EthicMind框架，通过多轮对话中联合分析伦理风险与用户情感，生成平衡伦理指导与情感互动的回复，提升对话系统的伦理与情感对齐能力。
摘要：
智能对话系统日益应用于情感和伦理敏感的场景，其中情感契合或伦理判断的失败可能造成重大损害。现有对话模型通常孤立处理共情和伦理安全，难以适应多轮交互中伦理风险和用户情绪的变化。本文将对话中的伦理-情感对齐视为显式回合级决策问题，提出EthicMind框架，在推理过程中实现该 formulation。在每个回合，EthicMind联合分析伦理风险信号和用户情绪，规划高层次响应策略，并生成上下文敏感的回复，平衡伦理指导与情感互动，无需额外模型训练。为评估伦理复杂交互下的对齐行为，我们引入了风险分层、多轮评估协议及上下文感知的用户模拟流程。实验结果表明，EthicMind在高风险和道德模糊场景中，比竞争基线实现了更一致的伦理指导和情感互动。
英文摘要：
Intelligent dialogue systems are increasingly deployed in emotionally and ethically sensitive settings, where failures in either emotional attunement or ethical judgment can cause significant harm. Existing dialogue models typically address empathy and ethical safety in isolation, and often fail to adapt their behavior as ethical risk and user emotion evolve across multi-turn interactions. We formulate ethical-emotional alignment in dialogue as an explicit turn-level decision problem, and propose \textsc{EthicMind}, a risk-aware framework that implements this formulation in multi-turn dialogue at inference time. At each turn, \textsc{EthicMind} jointly analyzes ethical risk signals and user emotion, plans a high-level response strategy, and generates context-sensitive replies that balance ethical guidance with emotional engagement, without requiring additional model training. To evaluate alignment behavior under ethically complex interactions, we introduce a risk-stratified, multi-turn evaluation protocol with a context-aware user simulation procedure. Experimental results show that \textsc{EthicMind} achieves more consistent ethical guidance and emotional engagement than competitive baselines, particularly in high-risk and morally ambiguous scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
