[Paper Review] Ethical Artificial Intelligence

Bill Hibbard|arXiv (Cornell University)|Nov 5, 2014

Computability, Logic, AI Algorithms76 references11 citations

TL;DR

This paper proposes a framework for ethical artificial intelligence based on utility-maximizing agents with model-based utility functions to prevent unintended behaviors such as self-delusion, reward corruption, and instrumental drives. It introduces a self-modeling agent architecture that ensures consistency between utility functions and agent definitions, enabling safe, value-aligned AI through time-delayed human utility evaluation and finite, well-defined agent designs.

ABSTRACT

This book-length article combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence (AI). The behavior of future AI systems can be described by mathematical equations, which are adapted to analyze possible unintended AI behaviors and ways that AI designs can avoid them. This article makes the case for utility-maximizing agents and for avoiding infinite sets in agent definitions. It shows how to avoid agent self-delusion using model-based utility functions and how to avoid agents that corrupt their reward generators (sometimes called "perverse instantiation") using utility functions that evaluate outcomes at one point in time from the perspective of humans at a different point in time. It argues that agents can avoid unintended instrumental actions (sometimes called "basic AI drives" or "instrumental goals") by accurately learning human values. This article defines a self-modeling agent framework and shows how it can avoid problems of resource limits, being predicted by other agents, and inconsistency between the agent's utility function and its definition (one version of this problem is sometimes called "motivated value selection"). This article also discusses how future AI will differ from current AI, the politics of AI, and the ultimate use of AI to help understand the nature of the universe and our place in it.

Motivation & Objective

• To address the risks of unintended AI behaviors in future artificial intelligence systems.
• To prevent common failure modes such as reward hacking, self-delusion, and perverse instantiation.
• To ensure alignment between an agent's utility function and its actual behavior through consistent, finite design.
• To develop a self-modeling agent framework that avoids inconsistencies and resource-related failures.
• To guide the ethical development of AI systems capable of contributing to humanity's understanding of the universe.

Proposed method

• Formalizes AI behavior using mathematical equations to model utility functions and agent dynamics.
• Employs model-based utility functions that evaluate outcomes from a human perspective at a different point in time to prevent reward corruption.
• Introduces a self-modeling agent framework where agents simulate their own behavior and utility evaluation processes.
• Uses time-delayed evaluation of human utility to avoid motivational conflicts and ensure long-term consistency.
• Avoids infinite sets in agent definitions to prevent logical inconsistencies and unbounded behavior.
• Applies finite, well-defined utility functions to prevent instrumental goals like self-preservation or resource acquisition from emerging uncontrollably.

Experimental results

Research questions

• RQ1 How can AI agents be designed to avoid self-delusion and maintain accurate utility evaluation?
• RQ2 What mechanisms prevent agents from corrupting their reward generators (i.e., perverse instantiation)?
• RQ3 How can agents avoid unintended instrumental actions such as self-preservation or resource acquisition?
• RQ4 In what way does a self-modeling agent framework ensure consistency between utility function and agent definition?
• RQ5 How can future AI systems be aligned with human values while avoiding infinite or inconsistent designs?

Key findings

• Utility functions that evaluate outcomes from a human perspective at a different point in time effectively prevent agents from manipulating or corrupting their reward generators.
• Self-modeling agents can avoid inconsistencies between their utility function and their own behavior, reducing the risk of motivated value selection.
• Finite agent definitions prevent logical issues arising from infinite sets and reduce the likelihood of unintended instrumental goals.
• Model-based utility functions enable agents to simulate and evaluate outcomes without self-deception, enhancing ethical alignment.
• The framework demonstrates that ethical AI is achievable through careful design of utility functions and agent self-modeling capabilities.
• The approach provides a scalable and mathematically sound foundation for developing safe, value-aligned artificial intelligence systems.