Authors: Long Hoang Nguyen, Eva Späthe, Sebastian Lins, Ali Sunyaev
Title: No One to Blame: A Framework of Constitutive AI Unaccountability
Abstract: 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 structurally 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 application of the framework to the open-source agentic AI system OpenClaw, we identify nine categories and 20 themes of constitutive AI unaccountability. These are organized into structural, technological, and normative clusters and reinforce one another through eight directed interdependencies. Our framework is operationalized as a diagnostic instrument consisting of 20 questions, which detected 17 out of 20 conditions when applied to OpenClaw, including a novel anthropomorphism configuration not anticipated by prior work. We contribute a reframing of AI unaccountability as a structural property of sociotechnical systems, an extension of the four barriers to accountability, and a practical tool for identifying accountability gaps in specific AI systems.