Understanding the Process of Human-AI Value Alignment

2025-09-17

AI Summary

This study addresses the conceptual ambiguity and theoretical fragmentation surrounding AI value alignment by redefining it as “a dynamic process of sustained coordination between humans and autonomous agents in expressing and realizing abstract values across diverse contexts,” explicitly accommodating cognitive limitations and cross-group ethical-political tensions. Through a systematic literature review and thematic analysis, we coded and clustered 172 recent core papers from the AI literature. This yielded six key research themes and enabled the construction of the first integrative conceptual framework for value alignment. The framework elucidates foundational challenges—including value representation, preference aggregation, and dynamic adaptation—while delineating critical pathways for human–AI co-evolution of values and identifying high-priority frontiers for empirical and theoretical advancement. It thus provides foundational support for both the systematic theorization and rigorous empirical investigation of value alignment.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Morality & Value-based AI
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Background: Value alignment in computer science research is often used to refer to the process of aligning artificial intelligence with humans, but the way the phrase is used often lacks precision. Objectives: In this paper, we conduct a systematic literature review to advance the understanding of value alignment in artificial intelligence by characterising the topic in the context of its research literature. We use this to suggest a more precise definition of the term. Methods: We analyse 172 value alignment research articles that have been published in recent years and synthesise their content using thematic analyses. Results: Our analysis leads to six themes: value alignment drivers & approaches; challenges in value alignment; values in value alignment; cognitive processes in humans and AI; human-agent teaming; and designing and developing value-aligned systems. Conclusions: By analysing these themes in the context of the literature we define value alignment as an ongoing process between humans and autonomous agents that aims to express and implement abstract values in diverse contexts, while managing the cognitive limits of both humans and AI agents and also balancing the conflicting ethical and political demands generated by the values in different groups. Our analysis gives rise to a set of research challenges and opportunities in the field of value alignment for future work.

Problem

• Defining precise human-AI value alignment process
• Analyzing 172 research articles for thematic synthesis
• Addressing cognitive limits and conflicting ethical demands

Innovation

• Systematic literature review methodology
• Thematic analysis of 172 articles
• Defining value alignment process