ABSTRACT
Modern artificial intelligence (AI) systems can act with a high degree of independence yet lack legal personhood. This paradox fractures doctrines grounded in human-centric notions of mens rea and actus reus. This Article introduces Operational Agency (OA) – a permeable legal fiction functioning as a structured 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 the 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 in complex scenarios. Across five real-world case studies spanning tort, civil rights, constitutional law, and antitrust, it demonstrates how OA and OAG resolve accountability challenges in diverse contexts ranging from autonomous vehicle collisions to algorithmic price-fixing, offering courts, legislatures, and industry a principled method to ensure human accountability keeps pace with technological autonomy—without conferring personhood on AI.
Mukherjee, Anirban and Chang, Hannah, Operational Agency: A Permeable Legal Fiction for Tracing Culpability in AI Systems (October 29, 2025).
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