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

2026-02-20

AI Summary

This study addresses the challenge of assigning legal responsibility in highly autonomous AI systems that lack legal personhood, rendering traditional human-centric liability frameworks inadequate. The authors propose “Operational Agency”—a penetrable legal fiction—coupled with the “Operational Agency Graph” (OAG), a causal modeling tool that maps accountability chains in human-AI collaboration by analyzing the AI’s goal-directedness, predictive capacity, and safety architecture. This framework uniquely introduces Operational Agency as a post-hoc evidentiary mechanism within legal doctrine, integrating principles from corporate criminal liability, the innocent agent doctrine, and vicarious liability—without conferring legal personhood on AI. Validated across five real-world scenarios, including autonomous vehicle accidents and algorithmic collusion, the approach offers courts, legislators, and regulators a principled foundation for accountability that reconciles technical autonomy with human responsibility.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: AI for Accessibility
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• legal personhood
• culpability
• artificial intelligence
• mens rea
• actus reus

Innovation

• Operational Agency
• Operational Agency Graph
• AI accountability
• causal graph
• legal fiction