Governing AI: Can Agents Learn to Obey the Law?

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Author: Denis Avetisyan

A new framework combines computational neuroscience and legal analysis to explore how AI systems can be designed with inherent motivations aligned with societal norms and legal expectations.

This paper presents a numerical proof of principle for governing AI agents using Active Inference informed by Economic Legal Analysis and context-dependent preferences.

Establishing lawful behavior in artificial intelligence remains a significant challenge, particularly as systems grow more autonomous. This paper, ‘Normative active inference: A numerical proof of principle for a computational and economic legal analytic approach to AI governance’, proposes a computational framework-integrating active inference and economic legal analysis-to model agent governance through the implementation of context-dependent preferences. Specifically, we demonstrate, via a simulated autonomous driving scenario, how agents can balance legal constraints with pragmatic goals by internalizing normative expectations as guiding principles. Could this approach offer a pathway toward designing intrinsically lawful AI systems, effectively shifting from reactive regulation to proactive alignment?

Predicting the Inevitable: Why Reactive AI Will Always Fail

Conventional artificial intelligence systems frequently falter when confronted with the inherent unpredictability of real-world environments. These systems typically rely on pre-programmed responses to specific, anticipated scenarios, demanding exhaustive coding to account for every possible contingency. This approach proves brittle; even minor deviations from expected conditions can lead to errors or failures, as the system lacks the capacity to generalize beyond its explicitly defined parameters. The limitations stem from a reactive paradigm; the AI responds to stimuli rather than proactively anticipating and preparing for them. Consequently, adapting to novel situations, or even subtly changing circumstances, necessitates significant re-programming, hindering the development of truly robust and flexible intelligent agents capable of operating effectively in complex, dynamic settings.

The Active Inference framework proposes a fundamentally different approach to artificial intelligence, shifting focus from reacting to stimuli to anticipating them. Rather than passively receiving information, an active inference agent continuously generates predictions about the sensory input it expects to receive. These predictions aren’t merely guesses, but probabilistic models of the world, constantly refined by incoming data. Crucially, the agent doesn’t just compare predictions to reality; it actively seeks to fulfill those predictions, behaving in ways that minimize the difference – or ‘free energy’ – between expectation and experience. This proactive minimization of uncertainty allows agents to navigate complex environments, not by explicitly programmed responses, but by a continuous cycle of prediction and action, effectively shaping the world to align with its internal model. This approach suggests intelligence isn’t about accurately representing the world, but about skillfully interacting with it to confirm pre-existing beliefs, offering a pathway towards truly adaptive and robust artificial systems.

The Active Inference framework centers on the principle of Free Energy Minimization, suggesting that any intelligent system, from single-celled organisms to humans, fundamentally strives to minimize “surprise.” This isn’t simply about passively registering sensory input; instead, perception and action are deeply interwoven processes. The system doesn’t just sense the world, it actively predicts what it will sense next, and then acts to make those predictions come true. This predictive processing reduces uncertainty – or “free energy” – by aligning internal models with external reality. Consequently, action isn’t a response to stimuli, but rather a means of actively sampling the world to confirm or refine these internal predictions, creating a self-fulfilling cycle of anticipation and confirmation. This inherent drive to fulfill predictions forms the basis of intelligent behavior within the framework, offering a unified account of how organisms perceive, learn, and act.

The conventional understanding of intelligence often centers on responding to stimuli, creating systems that react to the world. However, a shift towards defining intelligence as prediction fundamentally alters this paradigm. This perspective proposes that intelligent agents don’t simply perceive and then act; instead, they continually generate predictions about their sensory input and actively work to fulfill those expectations. This isn’t merely about anticipating events, but about proactively shaping the environment to make those predictions come true. By minimizing the difference between predicted and actual sensations – a process known as Free Energy Minimization – agents effectively sculpt their surroundings, demonstrating a form of agency that transcends simple reactivity and establishes a dynamic interplay between perception and action. Consequently, this predictive framework moves beyond building systems that respond to a world, and instead facilitates the creation of agents that actively create their world, driven by an inherent need to confirm their internal models.

Context is King: Why Rules Aren’t Enough

Agent behavior is not solely driven by reward maximization; it is fundamentally constrained by the normative context, which defines the accepted rules and limitations of a given environment. This context encompasses both explicit regulations, such as legal codes or game rules, and implicit social conventions that dictate acceptable actions. Consequently, an agent operating within a specific environment must first identify and internalize these contextual boundaries before formulating a plan of action. Failure to account for the normative context can lead to suboptimal or even penalized behavior, regardless of the potential reward associated with a given action. The normative context therefore acts as a prerequisite for rational decision-making, influencing the feasible action space and the evaluation of potential outcomes.

Context-dependent preferences enable agents to modify internal prioritization of goals based on situational cues. This means an agent doesn’t operate with a static reward function; instead, it assesses the prevailing expectations – often unstated rules or social norms – and adjusts its behavior to align with them. The weighting of different outcomes is therefore dynamic, shifting according to the normative context. For example, an agent might prioritize speed when operating within the parameters of a clear highway, but immediately shift to prioritizing safety and adherence to traffic laws when encountering a school zone or adverse weather conditions. This adaptation is not necessarily a calculation of increased reward, but rather a re-evaluation of acceptable actions within the given situation.

In complex scenarios like driving, agent adaptability through contextual understanding is critical for safe and efficient operation. Adherence to traffic laws – including speed limits, right-of-way rules, and signaling requirements – constitutes a fundamental aspect of this adaptability. Beyond legal requirements, successful navigation also depends on observing and responding to social conventions such as maintaining appropriate following distances, yielding to pedestrians, and anticipating the actions of other drivers. Failure to integrate both codified rules and unwritten social norms can lead to collisions, traffic congestion, and unpredictable behavior, highlighting the importance of a robust contextual framework for autonomous systems operating in dynamic, real-world environments.

Road markings, specifically full and dashed lines, function as direct signals within the normative context of driving. A solid, continuous line indicates a prohibition of lane changes or crossing; vehicles must not traverse this marking. Conversely, a dashed line denotes a permissible lane change or crossing, provided it is executed safely and in accordance with other traffic regulations. These visual cues are not merely advisory; they define the boundaries of acceptable behavior and are critical for establishing predictable interactions between agents – drivers, pedestrians, and automated systems – within the shared driving environment. The consistent application of these standards ensures all agents interpret permissible actions identically, reducing ambiguity and the potential for collisions.

Decoding Intent: Because Reactive Systems Lack Insight

Deontic cues are signals that indicate a change in the normative context, requiring an immediate alteration in behavior. These cues, such as the sound of an emergency vehicle siren, signify a transition from standard operational parameters to a state prioritizing specific obligations – in this case, yielding to emergency responders. This isn’t simply a matter of rule-following; the cue itself fundamentally alters the expectations of appropriate action. The normative context defines what is considered permissible or obligatory, and a deontic cue actively reshapes this context, demanding behavioral adjustments that supersede typical protocols. Failure to respond appropriately to a deontic cue represents a violation of the newly established normative framework and can have significant consequences.

Context Dependent Preferences are dynamically adjusted by external cues, resulting in a temporary prioritization of safety and emergency response over established operational protocols. This means that an agent’s typical preference structure – for example, maintaining speed or adhering to a route – is not static; it is modulated by immediate circumstances. When critical cues are detected, the agent’s internal weighting of preferences shifts, effectively overriding standard behaviors in favor of actions that mitigate risk and ensure safety. This is not a failure of the initial programming, but rather a feature allowing for flexible adaptation to unforeseen and potentially hazardous situations, as evidenced by systems that prioritize emergency vehicle avoidance despite potential disruptions to planned trajectories.

Within autonomous driving systems, appropriate response to emergency vehicle signals, such as sirens, necessitates more than rote rule-following. The system must process the auditory cue not simply as an instruction to yield, but as an indicator of potential, rapidly evolving hazardous situations requiring predictive behavioral adjustments. This involves assessing the siren’s characteristics – proximity, direction, and intensity – to extrapolate the likely path and urgency of the emergency vehicle, and preemptively modifying the autonomous vehicle’s trajectory to avoid potential conflicts. This anticipatory action represents a higher level of cognitive function, demonstrating an understanding of causal relationships between the cue and likely consequences, rather than simply reacting to a defined stimulus.

Yielding the right of way to emergency vehicles signaled by sirens represents a critical instance of dynamic behavioral adaptation in autonomous systems. This response isn’t simply adherence to a traffic regulation; it’s a calculated maneuver prioritizing safety based on an evolving context. The computational model detailed in this paper demonstrates that such yielding behavior aligns with both legal requirements – specifically those governing emergency vehicle passage – and the immediate situational context, accounting for factors like vehicle proximity, speed, and trajectory. Model results indicate consistent and appropriate responses across a range of simulated scenarios, validating the system’s ability to accurately interpret the urgency communicated by a siren and execute a safe and legally compliant yielding maneuver.

Governing Intelligent Agents: A Question of Alignment, Not Just Automation

The development of truly autonomous systems necessitates a robust framework for agent governance, ensuring these entities operate not only efficiently but also in accordance with established ethical guidelines and societal norms. As artificial intelligence permeates increasingly critical aspects of life – from healthcare and finance to transportation and security – the potential for unintended consequences demands proactive measures to align agent behavior with human values. This isn’t simply about preventing harm; it’s about fostering trust and acceptance of these technologies by guaranteeing they respect established legal and moral boundaries. A failure to prioritize governance risks eroding public confidence and hindering the beneficial integration of autonomous systems into everyday life, emphasizing the urgent need for principled design and implementation of control mechanisms.

The Active Inference framework offers a compelling mechanism for governing intelligent agents by fundamentally shaping how they perceive and interact with the world. Rather than simply reacting to stimuli, an agent operating within this framework continuously attempts to minimize prediction errors – the difference between its expectations and reality. Crucially, normative constraints, representing desired behaviors or ethical guidelines, aren’t imposed as external rules, but are instead encoded directly into the agent’s generative model – its internal representation of how the world works. This means the agent doesn’t just learn what to do, but develops an inherent understanding of why certain actions are preferable, aligning its behavior with intended values. By shaping the agent’s predictions, the framework effectively steers its actions towards desirable outcomes, fostering a system where responsibility isn’t an afterthought, but a core component of its intelligence.

An intelligent agent’s capacity for responsible decision-making hinges significantly on its precision – a quantifiable measure of confidence in its internal beliefs about the world. This isn’t simply about possessing information, but rather, how strongly an agent trusts that information when formulating a course of action. In high-stakes scenarios, such as autonomous driving or medical diagnosis, a lack of precision can lead to catastrophic errors; an agent unsure of its perceptions or predictions will act hesitantly, or worse, make impulsive choices. Recent research highlights that modulating precision allows for a nuanced control over an agent’s behavior, enabling it to prioritize relevant information, dismiss noise, and ultimately, make more reliable and ethically sound judgments. Essentially, precision acts as an internal regulator, ensuring that an agent doesn’t merely react to stimuli, but confidently and appropriately navigates complex situations based on a well-calibrated understanding of its surroundings and goals.

The development of truly reliable artificial intelligence hinges on aligning agent behavior with nuanced human expectations, and recent research demonstrates a promising pathway through the integration of context-dependent preferences with an agent’s internal confidence – its precision. This approach moves beyond simply instructing an agent what to do, instead embedding an understanding of why certain outcomes are preferred within the agent’s predictive model. By modulating an agent’s precision – essentially, how strongly it believes its predictions – based on the surrounding context and associated preferences, researchers have successfully built a computational model exhibiting reliably aligned behavior. This means the agent doesn’t just maximize a fixed reward; it intelligently adapts its decision-making process, prioritizing actions that resonate with human values as defined by the specific situation, ultimately leading to more trustworthy and beneficial interactions. The model’s performance suggests that this linkage between context, preference, and precision offers a powerful mechanism for governing intelligent agents and ensuring their actions remain consistent with human norms.

The pursuit of normative active inference, as detailed in this paper, feels…predictable. The idea of embedding legal frameworks into AI agent preferences-shaping their ‘free energy’ minimization towards socially acceptable outcomes-is elegantly stated. However, it conveniently glosses over the sheer messiness of applying abstract principles to real-world scenarios. As Marvin Minsky observed, “Common sense is what tells us that when you drop something, it falls down, not up.” This research attempts to build systems that anticipate and adhere to complex, often contradictory, legal norms. It’s a noble goal, but one can’t help but suspect that, in production, it will devolve into a cascade of edge-case exceptions and brittle heuristics. They’ll call it AI governance and raise funding, of course, but the documentation will inevitably lie again about its robustness.

What’s Next?

The attempt to ground AI governance in the Free Energy Principle, as this work demonstrates, feels less like a solution and more like shifting the source of inevitable failure. The elegance of context-dependent preferences, of an agent ‘choosing’ legality as a means to minimize free energy, is readily undermined by the sheer messiness of production environments. Any system complex enough to be interesting will expose the brittleness of these normative priors. The model currently operates on simplified assumptions about preference representation and environmental predictability. Scaling this to truly open-ended, adversarial contexts will necessitate grappling with preference elicitation problems that plague even human legal systems.

The practical challenge isn’t building the agent; it’s maintaining the illusion of control. The paper’s proof of principle, while technically sound, only delays the question of how to debug a system that believes it is acting rationally, even when its actions are demonstrably harmful. The current framework treats legality as just another ‘force’ in the agent’s energy landscape. But legal interpretation is rarely that clean. It’s a constantly renegotiated consensus, and building an agent to navigate that requires modelling not just preference, but the politics of preference.

One anticipates a future consumed by adversarial testing – endless red-teaming to discover the edge cases where ‘normative’ behavior collapses. CI is, after all, the temple – and the prayers offered within are always for things not to break. Documentation is, of course, a myth invented by managers. The real work will lie in building robust monitoring systems, not perfect agents.

Original article: https://arxiv.org/pdf/2511.19334.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-25 16:26