English(EN) Responsibility in Multi-Agent Sequential Decision-Making: Comparing Human Judgments to Formal Models of Causal Attribution

人工智能责任的形式化模型与人类判断的比较

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-05 00:55

研究人员借鉴因果归因框架，开发了多主体人工智能系统中责任归属的形式化模型。他们进行了一项大规模调查，使用修改版的Goofspiel纸牌游戏，将这些形式化模型与人类在顺序决策场景中的责任判断进行比较。虽然没有单一的形式化方法能完全匹配人类的直觉，但该研究确定了影响人类责任感知的关键因素，例如特定于代理的偏见以及代理可获得的信息。 AI

影响这项研究可能为开发更透明、更负责任的人工智能系统提供信息，尤其是在涉及多个交互代理的场景中。

排序理由该集群包含一篇学术论文，详细介绍了关于人工智能责任归属的新研究。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

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arXiv cs.MA (Multiagent) TIER_1 English(EN) · Goran Radanović · 2026-08-05 00:55

多智能体序列决策中的责任归属：人类判断与因果归因形式化模型比较

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{respon…

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多智能体序列决策中的责任归属：人类判断与因果归因形式化模型比较

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