Operational Agency: A Permeable Legal Fiction for Tracing Culpability in AI Systems

• URL: http://arxiv.org/abs/2602.17932v1
• Date: Fri, 20 Feb 2026 01:49:03 GMT
• Title: Operational Agency: A Permeable Legal Fiction for Tracing Culpability in AI Systems
• Authors: Anirban Mukherjee, Hannah Hanwen Chang,
• Abstract summary: This Article introduces Operational Agency (OA)-a permeable legal fiction structured as an ex post evidentiary framework-and Operational Agency Graph (OAG)<n>OA evaluates an AI's observable operational characteristics.<n>OAG operationalizes that analysis by embedding these characteristics in a causal graph to trace and apportion culpability among developers, fine-tuners, deployers, and users.
• Score: 0.31061678033205636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern artificial intelligence (AI) systems act with a high degree of independence yet lack legal personhood-a paradox that fractures doctrines grounded in human-centric notions of mens rea and actus reus. This Article introduces Operational Agency (OA)-a permeable legal fiction structured as an ex post evidentiary framework-and Operational Agency Graph (OAG), a tool for mapping causal interactions among human actors, organizations, and AI systems. OA evaluates an AI's observable operational characteristics: its goal-directedness (as a proxy for intent), predictive processing (as a proxy for foresight), and safety architecture (as a proxy for a standard of care). OAG operationalizes that analysis by embedding these characteristics in a causal graph to trace and apportion culpability among developers, fine-tuners, deployers, and users. Drawing on corporate criminal liability, the innocent-agent doctrine, and secondary and vicarious liability frameworks, the Article shows how OA and OAG strengthen existing doctrines. Across five real-world case studies spanning tort, civil rights, constitutional law, and antitrust, it demonstrates how the framework addresses challenges ranging from autonomous vehicle collisions to algorithmic price-fixing, offering courts a principled evidentiary method-and legislatures and industry a conceptual foundation-to ensure human accountability keeps pace with technological autonomy, without conferring personhood on AI.

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