Governing the Rise of AI: Beyond the Rules

by

Author: Denis Avetisyan

Effective AI oversight demands a fundamental shift from debating what regulations are needed to building the legal systems that will actually deliver them.

This paper argues for a focus on establishing robust legal infrastructure – including registration regimes and regulatory markets – to govern transformative AI and ensure accountability.

Despite growing attention to artificial intelligence governance, efforts largely concentrate on defining what rules are needed, overlooking the crucial how of implementation. This paper, ‘Legal Infrastructure for Transformative AI Governance’, argues that establishing robust legal and regulatory infrastructure-including registration regimes for advanced models and markets for AI regulatory services-is paramount given AI’s rapidly evolving capabilities. Specifically, the analysis proposes frameworks to not only dictate permissible AI development but also to facilitate the ongoing generation and enforcement of adaptive rules. Will proactively building this infrastructure prove essential to harnessing the benefits of transformative AI while mitigating its risks?

The Evolving Governance Landscape: Navigating the Risks of Intelligent Systems

The accelerating development of artificial intelligence, specifically the rise of sophisticated AI Agents and Generative AI models, is creating a significant challenge for policymakers worldwide. These systems, capable of autonomous action and content creation, are evolving at a pace that current legal and ethical frameworks simply cannot match. While regulations often lag behind technological innovation, the speed at which these AI systems are improving – demonstrating abilities previously confined to science fiction – has created a particularly acute “governance gap.” This disparity isn’t merely a matter of updating existing laws; it necessitates a fundamental rethinking of how accountability, safety, and societal impact are addressed in the context of increasingly intelligent and independent machines. The result is a rapidly evolving landscape where the potential benefits of AI are tempered by substantial, and growing, risks that remain largely unaddressed by established oversight mechanisms.

The escalating deployment of artificial intelligence systems has triggered a wave of legislative responses, reflecting growing concern over algorithmic bias and the potential for unintended consequences. As of mid-2025, over 1000 bills related to AI have been introduced at the state level across the United States, signaling a proactive, though fragmented, attempt to govern these powerful technologies. These proposed laws address a broad spectrum of issues, from data privacy and algorithmic transparency to the responsible use of AI in critical sectors like healthcare and criminal justice. The sheer volume of legislation underscores a recognition that AI systems, while promising significant benefits, are not neutral tools and can perpetuate or amplify existing societal biases if left unchecked, necessitating careful consideration and robust governance frameworks to mitigate potential harms.

Despite a surge of global attention and the issuance of 84 statements on responsible AI by 2019, existing governance frameworks are proving inadequate for the challenges presented by increasingly autonomous AI systems. These systems, capable of independent action and learning, introduce risks that traditional regulatory approaches – designed for static, programmed behaviors – simply cannot address. The core difficulty lies in predicting and controlling the emergent behaviors of AI agents operating without constant human oversight, leading to concerns about unintended consequences and accountability when errors occur. This gap between technological advancement and regulatory preparedness is particularly acute with the rise of AI agents and generative AI, which can adapt, evolve, and operate with a level of independence previously unseen, demanding a fundamental rethinking of how these powerful technologies are governed and overseen.

Foundations for Responsible Innovation: Constructing a Legal Framework

Effective AI governance necessitates a comprehensive legal infrastructure comprised of three core elements: formal laws defining permissible AI applications and outlining liability frameworks; dedicated institutions responsible for enforcement, oversight, and the establishment of technical standards; and clearly defined processes for auditing AI systems, addressing grievances, and ensuring accountability. This infrastructure must address areas such as data privacy, algorithmic bias, intellectual property rights, and safety protocols to facilitate responsible AI development and deployment. Without a foundational legal framework, the potential benefits of AI cannot be realized while simultaneously mitigating associated risks to individuals and society. The absence of clearly defined rules and enforcement mechanisms creates uncertainty for developers, hinders innovation, and leaves citizens vulnerable to potential harms.

Traditional command-and-control regulation, characterized by prescriptive rules detailing precisely how AI systems must function, frequently exhibits limitations in the context of rapid technological advancement. These regulations often rely on predefined standards that quickly become outdated as AI capabilities evolve, necessitating frequent and often lengthy amendment processes. This inflexibility can stifle innovation by imposing undue burdens on developers and hindering the deployment of novel AI applications. Furthermore, the static nature of these rules struggles to address unforeseen risks or edge cases that emerge with increasingly complex AI systems, potentially creating regulatory gaps and hindering effective oversight. The reactive nature of these regulations, addressing issues after they arise, contrasts with the proactive demands of managing a dynamic field like artificial intelligence.

Performance-based regulation in AI prioritizes demonstrable outcomes rather than prescriptive technical specifications, allowing innovation to proceed without being stifled by outdated rules. This approach defines acceptable performance levels for AI systems – concerning accuracy, fairness, security, and robustness – and holds developers accountable for achieving these standards. Crucially, this must be coupled with proactive Risk Management Systems which involve identifying, assessing, and mitigating potential harms throughout the AI system’s lifecycle, from design and development to deployment and monitoring. These systems necessitate continuous evaluation, data collection on real-world performance, and the implementation of corrective actions when performance falls below defined thresholds, fostering an iterative and adaptable regulatory framework.

Refining Oversight: Innovative Approaches to Regulation

Registration regimes for frontier AI models and agents establish a formal process by which developers disclose details about their systems to regulatory bodies. These disclosures typically include model architecture, training data sources, intended use cases, and documented safety testing procedures. The primary benefit of such regimes is enhanced transparency, allowing regulators and the public to better understand the capabilities and potential risks associated with these advanced AI systems. This, in turn, facilitates more effective oversight, enabling regulators to monitor compliance with safety standards, identify potential harms, and enforce appropriate safeguards. Registration also promotes accountability by clearly identifying the entities responsible for the development and deployment of these models, and providing a mechanism for addressing any adverse consequences that may arise.

Regulatory Markets propose a shift from direct governmental oversight to a system leveraging Regulatory Service Providers (RSPs). These RSPs would perform regulatory functions – such as auditing, testing, and certification – on behalf of governing bodies, offering a potentially scalable solution to address the rapid development and deployment of complex technologies. This approach aims to increase efficiency by distributing the workload and fostering specialization, allowing regulators to focus on policy and oversight of the RSP system itself. The economic model relies on entities seeking certification or compliance paying the RSPs directly, creating a market-driven incentive for innovation and responsiveness within the regulatory process. Key to successful implementation is establishing clear standards, accreditation processes for RSPs, and mechanisms for ensuring their independence and impartiality.

Data portability is a foundational requirement for effective regulatory markets centered around Regulatory Service Providers (RSPs). It ensures that AI model developers and deployers can seamlessly transfer data – including model weights, training data provenance, and audit trails – between different RSPs. This interoperability fosters competition among RSPs, driving innovation and potentially lowering compliance costs, as developers are not restricted to a single provider. Without data portability, vendor lock-in becomes a significant risk, limiting developer choice and potentially hindering the development of specialized or more efficient regulatory services. Specifically, the ability to easily export and import data in a standardized format allows developers to switch RSPs, utilize multiple RSPs for different aspects of compliance, or even build their own internal regulatory capabilities based on portable data assets.

Navigating the Ethical Horizon: Accountability and Resilience in AI

As artificial intelligence agents gain increasing autonomy – the capacity to act independently and make decisions without direct human intervention – fundamental questions arise regarding legal personhood and accountability. Traditionally, legal responsibility has rested with human actors, but an AI agent’s independent actions challenge this framework. Determining liability when an autonomous system causes harm becomes exceptionally complex; is it the programmer, the owner, or the AI itself that should be held accountable? The existing legal structures, designed for human agency, struggle to address situations where an AI operates beyond pre-programmed parameters, potentially creating a gap in legal recourse. This necessitates exploring novel legal concepts, such as attributing limited legal personhood to AI or establishing new frameworks for shared responsibility, to ensure both innovation and public safety are adequately protected as these systems become more prevalent.

Before advanced AI agents are widely deployed, comprehensive security evaluations – notably through a practice called Red Teaming – are paramount. This involves simulating real-world attacks by skilled security professionals who attempt to breach the system’s defenses and expose vulnerabilities. Unlike traditional testing which verifies functionality, Red Teaming proactively seeks weaknesses in logic, code, and implementation, revealing potential exploits before malicious actors can discover them. These exercises aren’t simply about finding bugs; they assess the entire system’s resilience, including its ability to detect, respond to, and recover from attacks. The insights gained from Red Teaming allow developers to fortify defenses, refine algorithms, and ultimately minimize the risk of unintended consequences or malicious exploitation, ensuring a more secure and reliable AI ecosystem.

Successfully integrating increasingly powerful artificial intelligence into society demands a multi-faceted approach centered on foresight and continuous assessment. Proactive regulation, established before widespread deployment, can establish ethical guidelines and legal frameworks for AI behavior, clarifying accountability and fostering public trust. However, regulation alone is insufficient; rigorous testing methodologies, such as Red Teaming – where experts deliberately attempt to breach system defenses – are essential to identify vulnerabilities and potential failure points. Crucially, this cannot be a one-time event; ongoing monitoring of deployed AI systems allows for the detection of emergent behaviors, adaptation to evolving threats, and continuous improvement of safety protocols, ultimately maximizing the benefits of AI while proactively minimizing associated risks and harms.

The pursuit of robust AI governance, as detailed in the paper, necessitates acknowledging the inherent ephemerality of any constructed system. This echoes Bertrand Russell’s observation: “The difficulty lies not so much in developing new ideas as in escaping from old ones.” The article posits a move from defining rules to building the infrastructure for their creation and adaptation – a rejection of static, pre-defined solutions. Any registration regime or regulatory market, however meticulously designed, will inevitably require revision as agentic AI evolves. The challenge isn’t merely establishing control, but cultivating a legal framework capable of gracefully accommodating change, accepting that improvement ages faster than expected and that rollback, in a complex system, is a journey back along the arrow of time.

What Lies Ahead?

The paper rightly pivots the conversation from the content of AI governance to the scaffolding that must support it. Yet, constructing this legal infrastructure is not a matter of simply layering new rules onto existing systems. It’s an exercise in anticipating failure-recognizing that any framework, however meticulously designed, will inevitably exhibit decay. The question isn’t whether these registration regimes and regulatory markets will work indefinitely, but how gracefully they will degrade. A focus on robust monitoring and adaptive capacity will be paramount, not as a preventative measure, but as a means of extending the lifespan of the inevitable.

The exploration of agentic AI introduces a particularly thorny problem. Existing legal concepts are predicated on attributing responsibility to identifiable actors. As agency diffuses within complex AI systems, the very notion of accountability becomes increasingly tenuous. The pursuit of ‘explainability’ may prove to be a distraction, a futile attempt to impose human-centric narratives on systems that operate according to fundamentally different principles. Perhaps the field should instead focus on building systems capable of absorbing the consequences of their own actions, effectively internalizing responsibility rather than assigning it.

Stability, it should be acknowledged, is often merely a delay of disaster. The pursuit of perfect foresight is a fallacy. The true challenge lies in designing systems that are resilient to unforeseen consequences, capable of adapting to changing circumstances, and-ultimately-accepting their own impermanence. The legal infrastructure for transformative AI will not be a fortress against chaos, but a carefully constructed system for managing its inevitable arrival.

Original article: https://arxiv.org/pdf/2602.01474.pdf

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

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2026-02-03 14:40