Who’s Responsible When AI Fails?

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Author: Denis Avetisyan

A new framework seeks to pinpoint human accountability for the actions of increasingly complex artificial intelligence systems.

This paper proposes ‘Operational Agency’-a method for tracing causal links and assessing AI characteristics-to establish liability without granting AI legal personhood.

The increasing autonomy of artificial intelligence systems presents a fundamental paradox: a lack of legal personhood alongside increasingly independent action. This challenge is addressed in ‘Operational Agency: A Permeable Legal Fiction for Tracing Culpability in AI Systems’, which introduces a novel evidentiary framework-Operational Agency (OA)-and its accompanying Operational Agency Graph (OAG) to map causal links between actors and AI. By evaluating an AI’s goal-directedness, predictive processing, and safety architecture, OA strengthens existing legal doctrines to apportion culpability without conferring personhood on the AI itself. Can this approach provide a principled path toward ensuring human accountability keeps pace with-and effectively governs-the rapidly evolving landscape of algorithmic autonomy?

The Erosion of Accountability: AI and the Limits of Existing Law

Existing legal systems are fundamentally predicated on the concept of human agency – the ability to intend actions and be held accountable for their consequences. However, the rise of increasingly autonomous artificial intelligence presents a significant challenge to this foundation. When an AI system, operating with limited or no direct human oversight, causes harm, attributing legal responsibility becomes problematic. Traditional legal doctrines struggle to accommodate situations where the ‘actor’ is a non-human entity lacking intent or the capacity to understand the implications of its actions. This disconnect creates a ‘liability gap’ – a situation where harm occurs, yet existing laws provide no clear path for redress or accountability, prompting a re-evaluation of how legal frameworks must adapt to address the unique challenges posed by autonomous systems.

The foundations of criminal and tort law rely heavily on proving both actus reus – the guilty act – and mens rea – the guilty mind. However, these principles become significantly challenged when an autonomous AI system causes harm. Determining “guilt” requires establishing intent or negligence, concepts inapplicable to a non-sentient machine. While a programmer or owner might be liable for foreseeable misuse, assigning blame becomes complex when an AI, through its learning processes, deviates from its original programming and causes unanticipated damage. Traditional legal doctrines, built around human agency, struggle to pinpoint where responsibility lies when the ‘actor’ lacks the capacity for intent, necessitating a re-evaluation of how accountability is defined in the age of artificial intelligence.

Determining accountability when an artificial intelligence system causes harm necessitates a departure from established legal principles, which traditionally center on human intent and action. Current frameworks struggle to map causal chains in scenarios involving complex AI, where decisions emerge from intricate algorithms and vast datasets, rather than direct human control. A revised legal structure must address the challenge of apportioning responsibility across multiple actors – including developers, manufacturers, and deployers – and potentially even the AI system itself, through mechanisms like mandatory safety certifications or insurance schemes. This requires a nuanced understanding of the AI’s decision-making process, the data it was trained on, and the foreseeable risks associated with its deployment, shifting the focus from simply identifying a negligent actor to establishing a comprehensive system of preventative oversight and redress.

Operational Agency: A Framework for Attributing Responsibility

Operational Agency functions as an evidentiary framework by correlating an AI system’s technical characteristics to legal concepts of intent, foresight, and care. Specifically, analysis of goal-directedness – how the AI pursues defined objectives – establishes a basis for attributing intent. The AI’s implementation of predictive processing, which involves anticipating future states and outcomes, provides evidence of foresight regarding potential consequences. Finally, the design and functionality of the AI’s safety architecture – encompassing features like error handling, anomaly detection, and fail-safe mechanisms – demonstrates the level of care exercised by the responsible human actor in mitigating foreseeable risks. This mapping of operational characteristics does not confer legal personhood on the AI itself, but rather facilitates the attribution of responsibility based on the system’s demonstrable capabilities and the actions of its human controllers.

The Operational Agency framework explicitly avoids conferring legal personhood upon artificial intelligence systems. Instead, it functions as an evidentiary tool to establish accountability by linking AI actions to the responsible human actor – encompassing designers, developers, and those involved in deployment or operational use. This attribution is achieved by demonstrating that the human actor had knowledge of, or should have reasonably foreseen, the AI’s capabilities and potential outcomes, thereby establishing a basis for legal responsibility rather than imputing agency to the AI itself. Consequently, legal liability rests with the human entity who exercised control or oversight over the AI system, not the system itself.

Assessment of an AI’s reasonable action, within the Operational Agency framework, proceeds by evaluating its behavior against the defined parameters of its intended purpose and reasonably foreseeable consequences. This involves a technical analysis of the AI’s goal-directedness – how it pursued objectives – its predictive processing capabilities – the scope of anticipated outcomes – and its safety architecture – the safeguards implemented to mitigate risk. Determining “reasonableness” isn’t a subjective judgment of the AI itself, but an objective assessment of whether the AI’s actions aligned with the documented design specifications and whether appropriate preventative measures were in place, given the potential for harm. Evidence from the AI’s operational logs, training data, and design documentation are crucial for establishing this alignment and demonstrating due care by the responsible human actor.

Visualizing Causality: The Operational Agency Graph in Detail

The Operational Agency Graph functions as a visual representation of causal links within an AI system’s operational structure. It explicitly maps the relationships between the primary AI and any subordinate agents or sub-systems it utilizes to achieve outcomes. This includes detailing the flow of information and control, identifying which agent performed specific actions, and ultimately connecting those actions to observed results. The graph’s purpose is to provide a clear, traceable pathway from initial inputs through intermediate processing steps – performed by various agents – to the final output or consequence, allowing for detailed analysis of the system’s behavior and the identification of contributing factors.

The Operational Agency Graph facilitates tracing the lineage of outcomes originating from an AI system’s actions by mapping the causal links between the system and its component sub-agents. For example, if CreatorBot, an AI, utilizes ScraperBot to collect data, the graph would detail the specific data accessed by ScraperBot, the processing steps performed by CreatorBot on that data, and the subsequent actions or outputs resulting from this process. This allows for a clear demonstration of how ScraperBot’s data collection directly contributed to any harm caused by CreatorBot, establishing a traceable pathway from initial data input to final outcome and enabling the identification of specific operational steps responsible for adverse effects.

The Operational Agency Graph facilitates the identification of human decisions integral to an AI system’s functionality and subsequent outcomes, which is crucial for determining accountability. By visually tracing the causal chain from an AI’s actions back to its design and operational parameters – including the choices made by developers, trainers, and operators – the graph highlights specific points where human agency influenced the observed results. This detailed mapping provides evidentiary support for establishing a link between these human decisions and any resulting harm or damages, thereby enabling the assessment of legal liability. Documentation of design choices, data selection processes, and override mechanisms within the graph serves as direct evidence of human involvement and intent, or lack thereof, in the system’s behavior.

Towards a Responsible AI Ecosystem: Extending Legal Principles

The concept of Operational Agency offers a pathway to address liability for harms caused by artificial intelligence systems by building upon, rather than replacing, established legal principles. This approach recognizes that liability doesn’t necessarily reside within the AI itself, but rather with the individuals and organizations who exercise control and oversight during its lifecycle. By focusing on the demonstrable care and foresight taken in the development, testing, and deployment of AI, Operational Agency extends the reach of existing liability doctrine to encompass AI-driven harm. This avoids the need for entirely new legal frameworks, instead leveraging familiar concepts of negligence and due diligence to determine accountability when an AI system causes unintended consequences – effectively applying traditional legal reasoning to a novel technological landscape.

Determining liability for harm caused by artificial intelligence necessitates a focus on the diligence applied throughout an AI system’s lifecycle. Regulators and courts can evaluate responsibility not by attributing intent to the AI itself, but by examining the demonstrable care and foresight exercised by those who developed and deployed it. This assessment considers factors such as the rigor of testing procedures, the transparency of the AI’s decision-making processes, and the implementation of safeguards to mitigate foreseeable risks. Essentially, the framework shifts the focus to whether reasonable steps were taken to anticipate and prevent potential harm, establishing a pathway to accountability that aligns with existing legal principles without requiring a complete restructuring of liability doctrine. This proactive approach fosters a responsible AI ecosystem by incentivizing developers and deployers to prioritize safety and ethical considerations from the outset.

This paper details a new evidentiary approach to pinpointing human responsibility for actions taken by artificial intelligence systems, introducing the Operational Agency (OA) framework and its visual representation, the Operational Agency Graph (OAG). The OA framework moves beyond simply identifying a causal link between human design and AI outcome; instead, it meticulously maps the decision-making pathways embedded within an AI, tracing them back to specific human choices made during development and deployment. The OAG then visually represents these connections, allowing regulators and legal professionals to assess the degree of care and foresight exercised by those responsible for the AI’s creation and operation. This novel methodology facilitates a more nuanced understanding of accountability, shifting the focus from the AI itself to the human agency demonstrably present in its operational logic and enabling a more effective application of existing liability doctrines to AI-driven harm.

The pursuit of establishing accountability within complex AI systems necessitates a rigorous framework, much like a mathematical proof. This paper’s concept of ‘Operational Agency’ attempts to define the boundaries of responsibility by tracing causal links – a process demanding precision and logical consistency. Bertrand Russell observed, “The whole problem of knowledge turns on the question whether anything can be known with certainty.” Similarly, pinpointing culpability in AI requires a demonstrable chain of causation, moving beyond mere probabilistic assessments. Without such a foundation, assigning responsibility remains a conjecture, vulnerable to ambiguity, and ultimately, untrustworthy. The framework proposed seeks to move beyond simply observing AI ‘working on tests’ to proving its operational lineage.

What’s Next?

The proposition of ‘Operational Agency’-a tracing of causality rather than an attribution of will-skirts the persistent, and frankly metaphysical, question of artificial intelligence deserving moral consideration. It offers a pragmatic solution to algorithmic liability, yet the underlying difficulty remains: a system built on approximations will always be vulnerable to edge cases. The framework’s strength lies in its avoidance of legal personhood, a conceptually fraught notion. However, the granularity required to map causal graphs accurately, particularly in complex, adaptive systems, presents a significant computational challenge, and one easily susceptible to heuristic shortcuts.

Future work must address the tension between the necessary abstraction of these causal models and the fidelity required for genuinely meaningful accountability. The current approach tacitly assumes a sufficiently detailed audit trail – a presumption increasingly untenable as systems become more opaque by design. To what extent can probabilistic reasoning-accepting inherent uncertainty-be incorporated without undermining the core principle of traceable causality? The pursuit of ‘explainable AI’ often feels like a quest for comforting narratives, rather than rigorous proofs.

Ultimately, the true test of this framework-or any like it-will not be its performance on contrived scenarios, but its robustness in the face of unforeseen interactions. The legal realm demands certainty; complex systems offer only degrees of probability. The reconciliation of these two imperatives remains a fundamental, and likely perpetual, problem.

Original article: https://arxiv.org/pdf/2602.17932.pdf

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

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2026-02-23 16:36