Designing a Safe Autonomous Artificial Intelligence Agent based on Human Self-Regulation

TL;DR

This paper proposes a novel approach to designing safe AI agents by modeling them after human self-regulation principles, aiming to prevent harmful outcomes as AI complexity increases.

Contribution

It introduces a framework based on human self-regulation to improve AI safety and provides specific design guidance for implementing these principles in AI systems.

Findings

Human self-regulation principles can inform AI safety design

Guidelines for integrating self-regulation into AI programming

Potential for reducing unintended harmful AI behaviors

Abstract

There is a growing focus on how to design safe artificial intelligent (AI) agents. As systems become more complex, poorly specified goals or control mechanisms may cause AI agents to engage in unwanted and harmful outcomes. Thus it is necessary to design AI agents that follow initial programming intentions as the program grows in complexity. How to specify these initial intentions has also been an obstacle to designing safe AI agents. Finally, there is a need for the AI agent to have redundant safety mechanisms to ensure that any programming errors do not cascade into major problems. Humans are autonomous intelligent agents that have avoided these problems and the present manuscript argues that by understanding human self-regulation and goal setting, we may be better able to design safe AI agents. Some general principles of human self-regulation are outlined and specific guidance for AI design is given.