Barriers and Pathways to Human-AI Alignment: A Game-Theoretic Approach

2025-02-09

AI Summary

This paper investigates the theoretical limits of aligning high-capability AI agents with human preferences under realistic constraints: absent common priors, non-ideal communication, and bounded rationality. It asks: when is alignment feasible, and when does it become computationally intractable? Methodologically, the work establishes a minimal-assumption game-theoretic framework and derives the first tight computational complexity bounds—both upper and lower—for the alignment problem in M-objective, N-agent settings. Results show that under full rationality, alignment is achievable in linear time; however, exponential growth in task or agent scale renders alignment fundamentally infeasible in practice. Introducing bounded rationality and noisy communication degrades convergence to exponential time. Key contributions include identifying critical feasibility thresholds for alignment and characterizing structural constraints—such as preference separability and communication bandwidth limits—that enhance practical alignment viability.

Technology Category

• Multiagent Systems: Adversarial Agents
• Humans and AI: Learning Human Values and Preferences
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Under what conditions can capable AI agents efficiently align their actions with human preferences? More specifically, when they are proficient enough to collaborate with us, how long does coordination take, and when is it computationally feasible? These foundational questions of AI alignment help define what makes an AI agent ``sufficiently safe'' and valuable to humans. Since such generally capable systems do not yet exist, a theoretical analysis is needed to establish when guarantees hold -- and what they even are. We introduce a game-theoretic framework that generalizes prior alignment approaches with fewer assumptions, allowing us to analyze the computational complexity of alignment across $M$ objectives and $N$ agents, providing both upper and lower bounds. Unlike previous work, which often assumes common priors, idealized communication, or implicit tractability, our framework formally characterizes the difficulty of alignment under minimal assumptions. Our main result shows that even when agents are fully rational and computationally emph{unbounded}, alignment can be achieved with high probability in time emph{linear} in the task space size. Therefore, in real-world settings, where task spaces are often emph{exponential} in input length, this remains impractical. More strikingly, our lower bound demonstrates that alignment is emph{impossible} to speed up when scaling to exponentially many tasks or agents, highlighting a fundamental computational barrier to scalable alignment. Relaxing these idealized assumptions, we study emph{computationally bounded} agents with noisy messages (representing obfuscated intent), showing that while alignment can still succeed with high probability, it incurs additional emph{exponential} slowdowns in the task space size, number of agents, and number of tasks. We conclude by identifying conditions that make alignment more feasible.

Problem

• Human-AI alignment conditions
• Computational complexity of alignment
• Feasibility of scalable alignment

Innovation

• Game-theoretic framework
• Linear time alignment
• Exponential slowdown analysis