The Decision Path to Control AI Risks Completely: Fundamental Control Mechanisms for AI Governance
Yong Tao
TL;DR
This paper argues that AI risks cannot be adequately controlled by traditional risk-based or purely ethical guidelines alone; instead, it proposes a decision-centric governance framework built on five pillars and six AI mandates (AIMs). AIMs 1–3 embed inside AI systems to constrain decisions, while AIMs 4–6 establish societal boundaries on AI rights and resources, together forming a comprehensive brake system for AI. The approach emphasizes distinguishing AI decisions from capabilities, maintaining a human-AI decision hierarchy, and enforcing ELRs through both internal AI mechanisms and external societal structures. The framework aims to reduce existential and operational AI risks by ensuring value alignment with users, compliance with ELRs, human interventions, controlled resource use, and governance of AI rights, with explicit attention to generative AI’s unique challenges. If adopted, this decision-based governance could significantly lower AI risk while guiding safe, responsible innovation across both physical and generative AI domains.
Abstract
Artificial intelligence (AI) advances rapidly but achieving complete human control over AI risks remains an unsolved problem, akin to driving the fast AI "train" without a "brake system." By exploring fundamental control mechanisms at key elements of AI decisions, this paper develops a systematic solution to thoroughly control AI risks, providing an architecture for AI governance and legislation with five pillars supported by six control mechanisms, illustrated through a minimum set of AI Mandates (AIMs). Three of the AIMs must be built inside AI systems and three in society to address major areas of AI risks: 1) align AI values with human users; 2) constrain AI decision-actions by societal ethics, laws, and regulations; 3) build in human intervention options for emergencies and shut-off switches for existential threats; 4) limit AI access to resources to reinforce controls inside AI; 5) mitigate spillover risks like job loss from AI. We also highlight the differences in AI governance on physical AI systems versus generative AI. We discuss how to strengthen analog physical safeguards to prevent smarter AI/AGI/ASI from circumventing core safety controls by exploiting AI's intrinsic disconnect from the analog physical world: AI's nature as pure software code run on chips controlled by humans, and the prerequisite that all AI-driven physical actions must be digitized. These findings establish a theoretical foundation for AI governance and legislation as the basic structure of a "brake system" for AI decisions. If enacted, these controls can rein in AI dangers as completely as humanly possible, removing large chunks of currently wide-open AI risks, substantially reducing overall AI risks to residual human errors.