No One to Blame: A Framework of Constitutive AI Unaccountability

2026-08-12

AI Summary

This study addresses the structural accountability gaps engendered by autonomous AI systems, which elude conventional accountability mechanisms. It introduces the concept of “constitutive AI unaccountability,” framing unaccountability not as a failure to be remedied but as an inherent property of sociotechnical systems, thereby extending existing theories of accountability barriers. Through a three-phase qualitative investigation—comprising concept-driven literature analysis, secondary analysis of interviews with 27 experts, and application of the emerging framework to the open-source system OpenClaw—the research identifies nine categories encompassing twenty constitutive unaccountability themes and maps eight interdependencies among them. The authors develop an operational diagnostic instrument comprising twenty targeted questions, which successfully detected seventeen unaccountability conditions in OpenClaw, offering a novel paradigm for evaluating accountability in AI systems.

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 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.

Problem

• AI accountability
• constitutive unaccountability
• sociotechnical systems
• autonomous AI
• accountability gaps

Innovation

• constitutive unaccountability
• sociotechnical systems
• AI accountability
• agentic AI
• diagnostic framework