The Decision Path to Control AI Risks Completely: Fundamental Control Mechanisms for AI Governance

TL;DR

This paper proposes a comprehensive architecture with five pillars and six control mechanisms to achieve complete human control over AI risks, aiming to establish a theoretical foundation for AI governance and legislation.

Contribution

It introduces a systematic control framework with AI mandates built into systems and society, addressing major AI risks and physical safeguards for the first time.

Findings

Developed five pillars and six control mechanisms for AI governance.

Outlined AI mandates for internal and societal control.

Highlighted physical safeguards to prevent AI circumvention.

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, we develop 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 user 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 AI or AGI 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 implemented, 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.