Understanding the Process of Human-AI Value Alignment

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.

💡 Research Summary

The paper tackles the problem of imprecise and inconsistent usage of the term “value alignment” in computer‑science research on artificial intelligence. To bring clarity, the authors conduct a systematic literature review of 172 peer‑reviewed papers that explicitly address the alignment of AI systems with human values. The search was performed in the Scopus database, limited to English‑language computer‑science articles published up to November 2023, and refined through a two‑stage screening of titles/abstracts and full texts. Papers that merely mention values in a non‑ethical sense or focus solely on governance or a single value were excluded, resulting in a corpus that spans theoretical proposals, methodological contributions, empirical studies, and review articles.

Using an inductive thematic analysis, a single researcher coded the abstracts, introductions, and conclusions of each paper with NVivo, creating codes from scratch and iteratively grouping them into categories and higher‑order themes. This process yielded six overarching themes:

1. Value‑Alignment Drivers & Approaches – motivations (technical vs. normative), research methods, and interdisciplinary contexts.
2. Challenges in Value Alignment – priority‑setting, value conflicts, transparency, and cognitive limits of humans and AI.
3. Values in Value Alignment – representation of abstract values, hierarchical modeling, cultural diversity, and value evolution during system operation.
4. Cognitive Processes in Humans and AI – how humans learn, apply, and contextualize values, and how these processes can be mirrored or supported in autonomous agents.
5. Human‑Agent Teaming – interaction protocols, communication of values and state, trust building, and joint decision‑making structures.
6. Designing and Developing Value‑Aligned Systems – stakeholder analysis, requirement elicitation, verification, testing, and practical implementation case studies.

Each theme is illustrated with the three most frequently coded papers, demonstrating the cross‑cutting nature of the literature. The authors observe that the prevalence of these themes has remained relatively stable over time, indicating they are core challenges rather than fleeting trends.

Building on this thematic synthesis, the authors propose a refined definition: Value alignment is an ongoing process between humans and autonomous agents that seeks to express and implement abstract values across diverse contexts while managing the cognitive limits of both parties and balancing conflicting ethical and political demands from different groups. This definition moves beyond static, goal‑oriented formulations to emphasize continual interaction, adaptation, and negotiation.

The paper also outlines a research agenda: (i) standardizing value representation and hierarchical modeling; (ii) integrating human cognitive models with AI learning architectures; (iii) developing mechanisms for resolving multi‑cultural and multi‑political value conflicts; (iv) creating real‑time value‑adjustment and feedback loops; and (v) establishing verification and certification frameworks for value‑aligned systems.

Limitations are acknowledged: reliance on a single coder may introduce bias; the corpus is restricted to English‑language computer‑science publications, potentially overlooking relevant work in other languages or disciplines; and the qualitative nature of the analysis calls for complementary quantitative metrics and empirical validation.

In sum, the study provides a comprehensive mapping of the value‑alignment research landscape, offers a clearer, process‑oriented definition, and charts concrete directions for future work, thereby furnishing both scholars and practitioners with a solid foundation for advancing human‑compatible AI.