人类对AI在代理、滥用和偏差中的因果归因
Human Attribution of Causality to AI Across Agency, Misuse, and Misalignment
AI总结：
研究探讨了人类在AI导致有害结果时对因果责任的归因，发现AI代理程度越高，责任归于AI；开发者虽远离事件但被归责高，用户责任降低；分解AI为大语言模型和代理组件时，代理部分更易被归责。
摘要：
AI相关的事件正变得越来越频繁和严重，从安全故障到恶意行为的滥用。在复杂情况下，确定导致不良结果的要素，即因果选择问题，是确立责任的关键第一步。本文研究了当AI系统参与有害结果时，因果链结构中大众对因果责任的看法。我们进行了人类实验，考察因果归因、责备、预见性和反事实推理的判断。我们的发现表明：(1)当AI代理程度中等（人类设定目标，AI决定手段）或高（AI设定目标和手段）时，参与者将更大的因果责任归于AI。然而，在低AI代理（人类设定目标和手段）的情况下，参与者将更大的因果责任归于人类，尽管他们与结果的时间距离较远，尽管两者都意图了结果，这表明了自主性的影响；(2)当我们在人类和AI之间交换角色时，参与者始终认为人类更具因果性，即使两者执行相同的行为；(3)开发者尽管在链中距离较远，但被判断为高因果性，减少了对人类用户但不减少对AI的因果归因；(4)将AI分解为大型语言模型和代理组件时，代理部分在链中被判断为更具因果性。总体而言，我们的研究提供了证据，说明人们如何感知AI在滥用和偏差场景中的因果贡献，以及这些判断如何与用户和开发者等关键角色互动。这些发现可以为AI导致的伤害设计责任框架，并揭示直观判断如何影响现实世界AI相关事件的社会和政策辩论。
英文摘要：
AI-related incidents are becoming increasingly frequent and severe, ranging from safety failures to misuse by malicious actors. In such complex situations, identifying which elements caused an adverse outcome, the problem of cause selection, is a critical first step for establishing liability. This paper investigates folk perceptions of causal responsibility in causal chain structures when AI systems are involved in harmful outcomes. We conduct human experiments to examine judgments of causality, blame, foreseeability, and counterfactual reasoning. Our findings show that: (1) When AI agency was moderate (human sets the goal, AI determines the means) or high (AI sets the goal and the means), participants attributed greater causal responsibility to the AI. However, under low AI agency (where a human sets both a goal and means) participants assigned greater causal responsibility to the human despite their temporal distance from the outcome and despite both agents intended it, suggesting an effect of autonomy; (2) When we reversed roles between human and AI, participants consistently judged the human as more causal, even when both agents perform the same action; (3) The developer, despite being distant in the chain, was judged highly causal, reducing causal attributions to the human user but not to the AI; (4) Decomposing the AI into a large language model and an agentic component showed that the agentic part was judged as more causal in the chain. Overall, our research provides evidence on how people perceive the causal contribution of AI in both misuse and misalignment scenarios, and how these judgments interact with the roles of users and developers, key actors in assigning responsibility. These findings can inform the design of liability frameworks for AI-caused harms and shed light on how intuitive judgments shape social and policy debates surrounding real-world AI-related incidents.
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。
