多智能体序贯决策中的责任归属：人类判断与因果归因形式模型的比较
Responsibility in Multi-Agent Sequential Decision-Making: Comparing Human Judgments to Formal Models of Causal Attribution
AI总结：
本研究对比了多智能体序贯决策中责任归因的形式模型与人类判断的契合度，通过大规模调查和Goofspiel实验识别出影响人类责任判断的关键因素。
AI中文摘要：
随着人工智能在高风险决策场景中应用的日益广泛，识别结果（尤其是失败结果）的原因并确定责任归属已成为关键问题。本研究基于实际因果性框架的责任归因形式定义，探究其与人类责任判断的契合度。为此，我们采用修改后的纸牌游戏Goofspiel，开展大规模调查以获取多智能体序贯决策场景中的人类责任判断。我们评估了多种责任归因方法，分析其与人类责任判断的契合度，并识别显著影响责任判断的因素。尽管没有单一责任归因方法能始终与人类反应契合，但研究结果明确了影响人类责任判断的关键因素，包括智能体特定偏差及智能体决策时可获取的信息量。
英文摘要：
With the growing adoption of artificial intelligence in high-stakes decision-making, identifying the causes of outcomes--particularly failures--and determining who is responsible has become a critical concern. In this work, we examine how well formal definitions of \textit{responsibility attribution}, grounded in the framework of \textit{actual causality}, align with human judgments of responsibility. To this end, we conduct a large-scale survey to elicit human judgments of responsibility in multi-agent sequential decision-making scenarios, using a modified version of the card game Goofspiel. We evaluate multiple responsibility attribution methods, assess their alignment with human judgments about responsibility, and identify factors that significantly shape responsibility judgments. While no single responsibility attribution method consistently aligns with human responses, our findings highlight key factors that influence human responsibility judgments, including agent-specific biases and amount of information available to agents during decision-making.
