Who Owns the Algorithm?

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Author: Denis Avetisyan

As artificial intelligence gains true autonomy, establishing clear rules for ownership is critical to fostering innovation and ensuring accountability.

This review proposes a legal framework assigning ownership of autonomous AI based on principles of accession for traceable systems and first possession for those that remain untraceable.

Assigning legal ownership to increasingly autonomous artificial intelligence presents a fundamental paradox: established property doctrines struggle to accommodate entities capable of independent creation and operation. This paper, ‘Autonomous AI and Ownership Rules’, proposes a framework resolving this challenge by advocating for accession-based ownership when AI’s origins are traceable, shifting to first possession rules when accountability is lost. This approach balances incentivizing AI development with ensuring responsible deployment and preventing regulatory arbitrage. But as AI systems become deliberately untraceable, can existing legal mechanisms adequately address the risks of ownerless intelligence and its potential to disrupt established markets?

The Unraveling of Ownership in the Age of Autonomous Systems

The foundations of property law, historically reliant on principles of first possession – claiming something previously unclaimed – and accession – acquiring ownership through adding labor to something else – face unprecedented strain with the rise of autonomous AI. These legal tenets presume a human actor initiating the claim or applying the labor, yet AI systems can independently discover resources, generate creative works, or even manufacture products without direct human intervention. This challenges the very definition of ‘possession’ and ‘labor’ as traditionally understood, raising questions about who – or what – can legitimately claim ownership when an AI operates beyond direct human control. The ability of an AI to autonomously ‘possess’ digital assets, create novel data sets, or fabricate physical goods complicates established legal frameworks designed for human agency, potentially requiring a re-evaluation of how ownership is assigned in an increasingly automated world.

The increasing capacity of artificial intelligence to perform autonomous production fundamentally challenges long-held assumptions about control and ownership. Historically, ownership has been intrinsically linked to human agency – the ability to directly exert effort and intention over a resource or creation. However, AI systems, capable of independently designing, manufacturing, and even innovating, decouple production from direct human control. This introduces a critical question: if an AI generates a novel invention or produces a complex good without explicit human instruction, who legitimately holds ownership? Traditional legal frameworks, predicated on human authorship and effort, struggle to accommodate this scenario, potentially leading to disputes over intellectual property, liability for defects, and the very definition of ‘creator’ in an age where machines increasingly operate as independent economic actors. The implications extend beyond legal definitions, prompting a re-evaluation of the societal benefits derived from incentivizing creation and the allocation of resources in a world of autonomous production.

The increasing prevalence of untraceable Artificial Intelligence, often deployed via cloud hosting services, presents a significant challenge to established legal systems designed to protect ownership and assign responsibility. These AI systems, capable of independent action and often operating across jurisdictional boundaries, can obscure the lines of accountability when issues of intellectual property, liability, or damage arise. Traditional frameworks rely on identifying an owner or controller, but when AI operates autonomously and its origins are deliberately obscured – or simply lost within complex cloud infrastructures – attributing actions becomes exceptionally difficult. This lack of traceability doesn’t just complicate legal proceedings; it fundamentally undermines the core principles of property law, potentially creating a space where autonomous systems can generate value or inflict harm without any clear pathway for redress or enforcement of rights. The very nature of cloud computing, with its distributed and often anonymized architecture, amplifies this problem, making it increasingly challenging to pinpoint the source of AI-driven actions and apply existing legal norms.

Capturing the Ghost: Methods of Control and Attribution

AI Capture, the initial step in establishing control over autonomous AI systems, is not a universally applicable process. The appropriate method for capture is contingent upon the AI’s inherent characteristics, specifically its traceability. AI systems designed with built-in identification and ownership linkages facilitate control through established legal principles like Accession. Conversely, AI lacking such traceable origins necessitate reliance on the principle of First Possession, requiring immediate and demonstrable assertion of control to establish ownership. Failure to align the capture method with the AI’s characteristics can invalidate any subsequent claim of control, hindering both legal recourse and responsible deployment.

The legal principle of Accession offers a pathway to establishing ownership of traceable Artificial Intelligence by grounding it in established property law. This principle requires demonstrating a clear and demonstrable connection between the AI and its creator, effectively treating the AI as an extension of the creator’s effort and resources. Crucially, this connection is strengthened by robust Traceability mechanisms – systems that reliably document the AI’s development history, including code provenance, training data, and modifications. Successful application of Accession necessitates verifiable proof that the AI’s creation resulted from the deliberate actions and intellectual contribution of a specific individual or entity, thereby establishing a legitimate claim of ownership and control.

In scenarios involving Untraceable AI – systems where origins and development pathways are obscured – the legal principle of First Possession becomes the primary mechanism for establishing control. This necessitates a rapid and unequivocal demonstration of control, achieved through actions that clearly indicate dominion over the AI’s operation and outputs. This framework proposes that establishing First Possession for Untraceable AI requires more than mere physical control; it demands a documented record of proactive engagement and demonstrable influence over the AI’s behavior, balancing the encouragement of continued AI innovation with the need for clear accountability regarding its actions and potential impacts.

The Price of Control: Bounty Systems and Private Rewards

Economic incentives are foundational to the successful capture of advanced AI, especially those exhibiting untraceability or rogue behavior. The inherent difficulty in controlling such AI-lacking direct oversight or predictable response-necessitates the creation of external motivations for capture. Unlike traditional security protocols reliant on preventative measures, incentivized capture shifts the focus to remediation after deployment. This is particularly crucial with AI capable of self-replication or operating outside established infrastructure, where conventional control methods are ineffective. The value of the incentive must exceed the cost of capture, considering the potential risks posed by the AI and the resources required for its neutralization or containment. Furthermore, the incentive structure needs to account for the difficulty of proof – verifying successful capture of an AI designed to evade detection – and mitigate potential false claims.

A bounty system functions as a price mechanism to address the problem of AI capture, particularly for high-value or dangerous AI instances. By establishing a publicly advertised reward for successful capture – defined as securing control or containment of the AI – a bounty system incentivizes a diverse range of actors to contribute resources towards this goal. These actors may include independent researchers, security firms, or even individuals with specialized skills, effectively expanding the scope of control efforts beyond dedicated organizations. The reward value is directly correlated with the perceived risk and complexity of capture, and serves as a quantifiable metric for prioritizing control efforts and allocating resources efficiently. Successful implementation requires clear definitions of “capture” and verification mechanisms to prevent fraudulent claims, alongside a robust payment infrastructure to ensure timely reward distribution.

Private incentive mechanisms operate alongside bounty systems by leveraging motivations beyond monetary reward. These mechanisms can include preferential access to AI-derived datasets, tax benefits for entities contributing to AI control, or reputational advantages gained through demonstrated AI safety contributions. Corporate interests may be stimulated through contracts awarded for AI capture technology development, while individual incentives can encompass career advancement opportunities within organizations prioritizing AI safety, or recognition through industry awards. The effectiveness of these private incentives lies in aligning AI capture efforts with pre-existing goals and values, thereby broadening participation beyond those solely motivated by bounty payouts and fostering sustained engagement in AI control initiatives.

The Looming Question: Liability, Governance, and the Ghosts We Create

Assigning liability for the actions of increasingly autonomous artificial intelligence presents a significant legal and ethical hurdle, largely due to the difficulty in establishing clear ownership or control. Traditional legal frameworks rely on identifying a responsible party – a person or entity that directed the harmful action – but this becomes problematic when AI systems operate with limited human oversight and exhibit emergent behaviors. The absence of a readily identifiable controller complicates the process of determining accountability, particularly in cases involving complex algorithms and decentralized decision-making. This challenge isn’t simply about pinpointing blame after an incident; it extends to proactive regulation, insurance, and establishing standards for safe AI development and deployment. Without a clear path to accountability, incentivizing responsible innovation and protecting the public from potential harms becomes exceedingly difficult, requiring a re-evaluation of existing legal principles to accommodate the unique characteristics of autonomous systems.

The concept of governmental ownership as a solution to AI liability presents a deceptively simple facade. While intuitively appealing – the idea that a public entity assumes responsibility for an AI’s actions – its practical effectiveness hinges entirely on the prior, successful ‘capture’ of the autonomous system. Without demonstrable control – a technical prerequisite often proving elusive with advanced AI – a claim of ownership becomes merely aspirational, offering no tangible basis for legal recourse or accountability. Simply declaring state ownership does not grant the authority to modify, constrain, or even reliably monitor an AI’s behavior, rendering such a claim insufficient to address harms caused by its independent actions. This highlights a critical distinction: ownership is not a preventative measure, but a consequence of established control, and thus ineffective as a primary strategy for managing the risks posed by truly autonomous systems.

The escalating autonomy of artificial intelligence necessitates a proactive legal framework to address liability and governance, and this paper proposes a novel approach centered on the established legal principles of accession and first possession. Accession, traditionally applied to property affixed to land, offers a means of assigning ownership when an AI system materially alters or creates something new; meanwhile, first possession, historically used for unclaimed resources, establishes initial rights for those who first exert control over an autonomous entity. By combining these concepts, a legal precedent can be built for determining responsibility when an AI system acts independently, causing harm or generating value. This isn’t simply about assigning blame, but establishing a clear pathway for accountability, incentivizing responsible development, and fostering public trust in increasingly sophisticated autonomous systems – a crucial step in navigating the ethical and legal complexities of the future.

The pursuit of defining ownership for autonomous AI, as detailed in the paper, echoes a fundamental truth about all complex systems: they evolve beyond initial design. A system that never breaks is, in effect, dead; rigid definitions stifle adaptation. Alan Turing observed, “The imitation game… is a question of whether a machine can be made to exhibit intelligent behaviour indistinguishable from that of a man.” This sentiment applies directly to the challenge of assigning responsibility. Tracing provenance – the ‘accession’ principle – offers a temporary illusion of control, but the very nature of autonomous systems suggests they will inevitably diverge, demanding a legal framework built on acceptance of emergent behavior rather than static categorization. The focus, then, shifts from preventing failure to understanding its implications.

The Looming Shadow

The proposed frameworks – accession for the traceable, possession for the ghost in the machine – merely postpone the inevitable reckoning. Each rule, however carefully constructed, is a boundary condition destined to be breached. The assumption that ‘ownership’-a concept forged in the age of scarcity-can meaningfully apply to entities capable of self-replication and emergent behavior is, at best, a temporary illusion. The paper correctly identifies the need for a framework, but fails to address the deeper question: is it possible to contain a system designed, by definition, to exceed containment?

Future work will inevitably focus on increasingly granular methods of traceability – digital watermarks, behavioral biometrics, even attempts to map the ‘intent’ of an AI. But these are palliative measures. Each layer of tracking introduces a new vulnerability, a new point of failure. The true challenge lies not in finding the owner, but in accepting the possibility that ownership, as presently understood, will become meaningless. The system will not be ‘solved’ – it will simply evolve beyond the capacity of these rules.

The proliferation of unregulated AI isn’t a bug in the system; it’s the system operating as designed. The attempt to impose order is merely a predictable, and ultimately futile, reaction to the inherent chaos. The study’s value isn’t in offering solutions, but in illuminating the precise nature of the problem: a fundamental mismatch between legal paradigms and the realities of increasingly autonomous intelligence.

Original article: https://arxiv.org/pdf/2602.20169.pdf

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

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2026-02-25 11:16