Author: Denis Avetisyan
A new framework proposes governing AI not by assessing potential harms, but by directly regulating the decisions these systems make.
This review argues for fundamental control mechanisms within AI and the need for legislation focused on AI decision-making processes.
Despite rapid advances in artificial intelligence, ensuring complete human control over emerging risks remains a significant challenge. This paper, ‘The Decision Path to Control AI Risks Completely: Fundamental Control Mechanisms for AI Governance’, proposes a systematic framework centered on controlling AI decisions through five pillars supported by six core mechanisms-ranging from value alignment to resource limitations-and implemented via actionable AI Mandates. By focusing on how AI arrives at conclusions, rather than solely assessing outcomes, this work establishes a theoretical foundation for proactive legislation and governance. Could a decision-based approach finally provide the ‘brake system’ needed to rein in AI’s potential dangers and minimize residual risks to levels comparable to human error?
Proactive Governance: Shaping the Trajectory of Artificial Intelligence
The accelerating development of artificial intelligence necessitates a shift from reactive regulation to proactive governance. As AI systems gain in capability – impacting areas from healthcare and finance to transportation and national security – the potential for both unprecedented benefit and significant risk increases exponentially. Simply addressing failures after they occur is no longer sufficient; a forward-looking approach is critical to ensure these powerful technologies are deployed responsibly and ethically. This demands anticipating potential harms – including bias, privacy violations, and unintended consequences – and establishing robust safeguards before widespread implementation. Effective governance isn’t about stifling innovation, but rather about channeling its trajectory to align with societal values and maximize positive outcomes for all stakeholders, creating a future where AI serves as a force for progress and well-being.
Conventional risk management strategies, designed for systems with predictable behavior and clear lines of accountability, prove inadequate when confronting the complexities of autonomous artificial intelligence. These agents, capable of independent decision-making and adaptation, present challenges that extend beyond the scope of established protocols; static checklists and reactive measures struggle to address emergent behaviors and unforeseen consequences. Unlike traditional systems where failures stem from component malfunction, AI risks often arise from the interaction of complex algorithms and the environments they inhabit – a dynamic interplay that demands continuous monitoring and anticipatory safeguards. The very nature of machine learning, where systems improve through experience, introduces an evolving risk profile that necessitates a shift from simply preventing known failures to proactively managing uncertainty and potential unintended outcomes.
Effective AI risk management demands a multi-faceted strategy that transcends conventional approaches. Rather than relying on reactive measures, a layered defense is crucial, beginning with the embedding of ethical principles into the very design of AI systems. This foundational layer must be reinforced by robust legal frameworks that address accountability and liability as AI capabilities advance. Crucially, this isn’t simply about adapting existing laws; proactive legislation is needed to anticipate and mitigate emerging risks. This three-line defense – ethics, law, and proactive policy – works synergistically; ethical guidelines inform legal development, while forward-thinking legislation establishes clear boundaries and incentivizes responsible innovation, ultimately fostering public trust and maximizing the benefits of artificial intelligence.
Deconstructing the AI Decision Process: A Blueprint for Governance
The AI decision process encompasses a sequence of stages, beginning with problem framing – the definition of goals and constraints – followed by data acquisition and preprocessing, model selection, and ultimately, the generation of alternative solutions or predictions. Understanding each of these stages is fundamental to effective governance because interventions at any point can influence the final outcome. Specifically, biases introduced during data collection, limitations inherent in the chosen model, or flawed framing of the initial problem can all lead to undesirable results. Governance mechanisms must therefore address not only the outputs of AI systems, but also the internal processes that determine those outputs, allowing for identification and mitigation of risks at each stage of decision-making.
Decision-Based AI Governance centers on the direct regulation of an AI system’s internal processes-specifically, problem framing, alternative generation, and decision selection-rather than solely focusing on output. This approach enables proactive alignment by establishing constraints and controls at the stages where biases are introduced or undesirable behaviors are formulated. By governing these internal mechanisms, developers and regulators can influence how an AI reaches a conclusion, providing a more robust and verifiable method of ensuring desired outcomes and mitigating risks compared to solely auditing final results. This allows for targeted interventions to correct flawed reasoning or prevent the consideration of unacceptable options before they manifest in observable actions, effectively increasing transparency and accountability.
Current AI governance strategies often prioritize defining desired outcomes, but a comprehensive approach necessitates controlling the internal reasoning and action processes that generate those outcomes. This framework proposes a shift towards governing how an AI system arrives at a decision, rather than solely focusing on what that decision is. The intent is to mitigate AI-specific risks by establishing controls that limit potential harms to a level comparable with those inherent in human error; this involves implementing safeguards throughout the AI decision pipeline to ensure reliability, predictability, and alignment with intended objectives, ultimately reducing the potential for unforeseen or undesirable consequences.
Layered Controls: Fortifying AI Systems Against Unforeseen Risks
AI Control Mechanisms represent a fundamental shift from reactive compliance to proactive risk mitigation in artificial intelligence systems. While ethical guidelines and legal frameworks establish boundaries for acceptable AI behavior, they often address issues post-occurrence. Control Mechanisms, conversely, are designed to prevent undesirable outcomes before they manifest. This involves implementing technical safeguards that monitor AI operation, detect anomalies, and enable intervention – either through human oversight or automated responses – to maintain system safety and alignment with intended objectives. This preventative approach is crucial for addressing unforeseen consequences and ensuring responsible AI deployment, particularly as systems increase in autonomy and complexity.
Human intervention options within AI control mechanisms are designed to allow for temporary or conditional overrides of autonomous AI actions, enabling human operators to correct errors, address unforeseen circumstances, or refine decision-making processes. These interventions range from simple adjustments to complex redirections of AI behavior and require clearly defined protocols for activation and deactivation to prevent unintended consequences. Complementing these options is the AI Off-Switch, a hard-stop mechanism intended as a failsafe to immediately halt all AI operations in critical situations where intervention is insufficient or impossible. The Off-Switch is distinct from routine shutdown procedures, prioritizing speed and certainty of cessation over graceful termination and requiring robust security measures to prevent unauthorized activation or deactivation.
Digitization of controls is critical for physical AI systems to enable timely and dependable human intervention. Traditional, manual overrides are insufficient due to the speed and complexity of AI-driven physical actions. Our framework proposes six fundamental control mechanisms, termed AIMs 1-6, to address this need. These AIMs provide a layered approach to oversight, encompassing functions such as remote monitoring of system status, adjustable operational parameters, immediate halt capabilities, constrained action spaces, prioritized intervention protocols, and comprehensive logging of all control actions. Implementing these digitized controls allows for a response time commensurate with the AI’s operational speed, reducing potential harm and ensuring predictable system behavior.
Establishing a Human-AI Hierarchy: A Foundation for Responsible Innovation
A robust Human-AI Hierarchy is paramount for responsible innovation, establishing a clear chain of command where human judgment consistently overrules automated processes. This isn’t simply about technical feasibility, but a fundamental design principle ensuring humans maintain ultimate control and oversight of all AI systems, even as their complexity increases. Such a framework necessitates carefully delineated roles and responsibilities, defining when and how AI can operate autonomously, and-critically-when human intervention is required. By prioritizing human agency, this hierarchy aims to prevent unintended consequences and ensures alignment with evolving ethical standards, fostering trust and accountability in increasingly intelligent machines. It moves beyond merely using AI to strategically governing it, safeguarding against scenarios where automated systems operate outside acceptable parameters or contradict core human values.
The imperative to align artificial intelligence with human values gains critical importance as AI systems navigate increasingly complex scenarios, demanding a robust ethical framework. Beyond simple task completion, modern AI is being deployed in contexts – from healthcare diagnostics to legal assessments and autonomous vehicles – where decisions carry significant moral weight. Establishing a clear value alignment isn’t merely about programming ethical guidelines; it necessitates anticipating unforeseen consequences and ensuring AI prioritizes human well-being, fairness, and transparency. Without this careful consideration, AI risks perpetuating existing biases, making unjust decisions, or operating in ways that erode public trust. Therefore, a proactive approach to embedding human values into AI design is fundamental to responsible innovation and the safe, beneficial integration of these powerful technologies into society.
A robust approach to managing artificial intelligence necessitates anticipating potential harms before they occur, and a proactive risk assessment framework, nested within a defined human-AI hierarchy, offers a pathway to achieve this. This paradigm moves beyond reactive problem-solving by systematically identifying vulnerabilities in AI systems – encompassing biases in training data, unintended consequences of algorithms, and security flaws – throughout the entire lifecycle of development and deployment. The ultimate goal isn’t to eliminate risk entirely – an impossible feat given the inherent complexity of both human endeavor and artificial intelligence – but rather to reduce those risks to a level comparable with residual human error, effectively establishing a safety threshold where AI-driven outcomes are demonstrably more reliable than unaided human judgment. By prioritizing preventative measures and integrating continuous monitoring, this framework seeks to foster innovation while simultaneously safeguarding against unforeseen and potentially detrimental consequences.
The pursuit of fundamental control mechanisms, as detailed in the paper, echoes a sentiment articulated by John von Neumann: “There is no possibility of absolute security.” This isn’t a statement of defeat, but rather a recognition that systems, particularly those as complex as evolving AI, require continuous adaptation. The paper’s emphasis on regulating AI decisions – establishing a human-AI hierarchy grounded in verifiable control – acknowledges the inherent imperfections of any static system. Just as a city’s infrastructure must evolve without wholesale rebuilding, so too must AI governance adapt to emergent behaviors, prioritizing structural integrity over the illusion of absolute prevention. The framework proposed isn’t about eliminating risk, but about managing it through clearly defined decision pathways and accountable control.
The Road Ahead
The proposition of governing artificial intelligence through the lens of decision-making, rather than solely through assessments of risk or alignment with stated values, reveals a fundamental truth: systems respond to incentives. Modifying one component – in this case, shifting regulatory focus – invariably triggers a cascade of consequences. The architecture of control, therefore, becomes paramount. Simply attempting to ‘align’ a complex system with human values is akin to rearranging deck chairs; the underlying structural dynamics remain untouched.
Unresolved, however, is the question of scalability. A decision-based framework demands granular oversight, a constant tracing of causality within increasingly opaque AI architectures. The challenge lies not merely in identifying the decisions, but in understanding the conditions that gave rise to those decisions. Current legislative efforts, largely focused on broad principles, fail to address this need for architectural awareness. Legislation which focuses on how an AI reaches a conclusion, rather than what that conclusion is, will necessitate a paradigm shift in auditing and accountability.
Future research must grapple with the inherent limitations of observation. Any attempt to monitor an AI’s decision-making process will, inevitably, alter that process. The pursuit of complete control, therefore, may prove to be a Sisyphean task. Perhaps the most fruitful path lies not in attempting to eliminate risk, but in designing systems that are inherently resilient to unforeseen consequences – systems that exhibit a degree of self-correction and adaptability, mirroring the elegance of natural systems.
Original article: https://arxiv.org/pdf/2512.04489.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-05 10:19