发表机构
CNR-ISTI（意大利国家研究委员会信息科学与技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该立场论文提出HCXAI应结合信息寻求心理学，分析其三类预期效用及相关认知偏差的两种失效模式，针对智能体AI系统的挑战，倡导设计让解释被用户主动寻求的HCXAI系统。
AI 中文摘要
本立场论文提出，以人为中心的可解释人工智能（HCXAI）应纳入信息寻求心理学的见解。基于Sharot和Sunstein的信息寻求动机框架，我们提出人们会根据三类预期效用判断是否使用解释：工具性效用（是否帮助我更好地行动）、享乐性效用（是否让我感觉更好）、认知性效用（是否提升我的理解）。每类效用都受已被充分证实的认知偏差影响，包括控制错觉、自动化偏差、不切实际的乐观主义、影响偏差、过度自信和确认偏差。这些偏差会导致两种失效模式：过度信息寻求，即注意力分散却未改善决策；以及信息寻求不足，即未审查关键风险和误解。对于智能体AI系统，这一挑战尤为严峻，此类系统的解释不仅要支持对单个输出的理解，还要支持对级联行动的预判、风险评估，以及决定何时弃权（不执行）。通过将信息寻求心理学整合到HCXAI中，我们倡导从提供解释转向让解释被寻求：设计考虑用户实际何时及为何想了解信息的系统。
英文摘要
This position paper argues that human-centered explainable AI (HCXAI) should incorporate insights from the psychology of information seeking. Drawing on Sharot and Sunstein's framework of information-seeking motives, we propose that people evaluate whether to engage with explanations based on three types of expected utility: instrumental (will it help me act better?), hedonic (will it make me feel better?), and cognitive (will it improve my understanding?). Each utility is estimated through a lens shaped by well-documented cognitive biases, including illusion of control, automation bias, unrealistic optimism, impact bias, overconfidence, and confirmation bias. These biases can lead to two failure modes: excessive information-seeking that fragments attention without improving decisions, and insufficient information-seeking that leaves critical risks and misunderstandings unexamined. This challenge is particularly acute for agentic AI systems, where explanations must support not just understanding a single output but anticipating cascading actions, assessing risks, and deciding when to intervene. By integrating information-seeking psychology into HCXAI, we advocate for a shift from making explanations available to making them sought: designing systems that account for when and why users actually want to know.
CommentsAccepted at the 6th Workshop on Human-Centered Explainable AI (HCXAI), CHI 2026
