发表机构
Technical University of Munich; University of Kassel（慕尼黑工业大学; 卡塞尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自主AI系统带来的问责挑战，该研究提出构成型AI不可问责性概念，构建诊断框架，在OpenClaw中检测到多数相关条件，为识别AI问责缺口提供实用工具。
AI 中文摘要
自主智能体AI系统的日益部署对传统问责机制构成了挑战。现有研究主要将AI问责缺口视为可通过更完善的标准、透明度和制度改革克服的障碍。我们认为这种框架是不够的：无论付出多少努力，特定的行动者、系统和制度配置会导致AI问责在概念上无法实现。我们引入构成型AI不可问责性的概念来描述这些配置。通过三阶段定性研究，包括以概念为中心的文献分析、对27名来自技术、法律和社会技术背景的AI专业人士的专家访谈的二次分析，以及对开源智能体AI系统OpenClaw的说明性框架应用，我们识别出构成型AI不可问责性的9个类别和20个主题。这些类别和主题分布在结构、技术和规范集群中，并通过8种定向相互依赖关系相互强化。我们的框架被实施为包含20个问题的诊断工具，应用于OpenClaw时检测到20种情况中的17种，包括一种倒置的拟人化配置，其中AI智能体是唯一可识别的行动者。我们的贡献在于将AI不可问责性重新定义为社会技术系统的构成属性，扩展了问责的四个障碍，并提供了一种用于识别特定AI部署中问责缺口的实用工具。
英文摘要
The increasing deployment of autonomous, agentic AI systems challenges traditional accountability mechanisms. Existing research predominantly frames AI accountability gaps as barriers that can be overcome through better standards, transparency, and institutional reform. We argue that this framing is insufficient: certain configurations of actors, systems, and institutions render AI accountability conceptually unachievable regardless of effort. We introduce the concept of constitutive AI unaccountability to capture these configurations. Through a three-stage qualitative study comprising a concept-centric literature analysis, a secondary analysis of 27 expert interviews with AI professionals from technical, legal, and sociotechnical backgrounds, and an illustrative framework application to the open-source agentic AI system OpenClaw, we identify nine categories and 20 themes of constitutive AI unaccountability. These are organized across structural, technological, and normative clusters and reinforce one another through eight directed interdependencies. Our framework is operationalized as a diagnostic instrument of 20 questions, which detected 17 of 20 conditions when applied to OpenClaw, including an inverted anthropomorphism configuration in which the AI agent was the only identifiable actor. We contribute a reframing of AI unaccountability as a constitutive property of sociotechnical systems, an extension of the four barriers to accountability, and a practical instrument for identifying accountability voids in specific AI deployments.
CommentsExtended version with appendix; final version to appear in the Proceedings of AAAI/ACM AIES 2026; v2: text encoding fix for author name, no content changes
