Normative active inference: A numerical proof of principle for a computational and economic legal analytic approach to AI governance

• URL: http://arxiv.org/abs/2511.19334v1
• Date: Mon, 24 Nov 2025 17:30:51 GMT
• Title: Normative active inference: A numerical proof of principle for a computational and economic legal analytic approach to AI governance
• Authors: Axel Constant, Mahault Albarracin, Karl J. Friston,
• Abstract summary: This paper presents a computational account of how legal norms can influence the behavior of artificial intelligence (AI) agents.<n>We propose that lawful and norm-sensitive AI behavior can be achieved through regulation by design, where agents are endowed with intentional control systems.<n>We conclude by discussing how context-dependent preferences could function as safety mechanisms for autonomous agents.
• Score: 0.6267988254367711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a computational account of how legal norms can influence the behavior of artificial intelligence (AI) agents, grounded in the active inference framework (AIF) that is informed by principles of economic legal analysis (ELA). The ensuing model aims to capture the complexity of human decision-making under legal constraints, offering a candidate mechanism for agent governance in AI systems, that is, the (auto)regulation of AI agents themselves rather than human actors in the AI industry. We propose that lawful and norm-sensitive AI behavior can be achieved through regulation by design, where agents are endowed with intentional control systems, or behavioral safety valves, that guide real-time decisions in accordance with normative expectations. To illustrate this, we simulate an autonomous driving scenario in which an AI agent must decide when to yield the right of way by balancing competing legal and pragmatic imperatives. The model formalizes how AIF can implement context-dependent preferences to resolve such conflicts, linking this mechanism to the conception of law as a scaffold for rational decision-making under uncertainty. We conclude by discussing how context-dependent preferences could function as safety mechanisms for autonomous agents, enhancing lawful alignment and risk mitigation in AI governance.

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