From Human Mind to Artificial Intelligence: Advancing AI Value Alignment Through Psychological Theories

Jin Shaoxiong; Liu Chao

doi:10.16719/j.cnki.1671-6981.20250402

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Journal of Psychological Science ›› 2025, Vol. 48 ›› Issue (4) : 782-791. DOI: 10.16719/j.cnki.1671-6981.20250402

Computational modeling and artificial intelligence

From Human Mind to Artificial Intelligence: Advancing AI Value Alignment Through Psychological Theories

Jin Shaoxiong^1,2,3, Liu Chao^1,2,3

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Abstract

In recent years, the field of artificial intelligence (AI) has witnessed unprecedented growth, characterized by major advancements in cognitive intelligence, perceptual processing, and decision-making capabilities. These technological breakthroughs have driven the widespread adoption of AI systems across a wide range of sectors, including healthcare, education, finance, and transportation. As a result, AI has become instrumental in improving operational efficiency, enhancing accuracy, and fostering innovation. There is little doubt that such developments have significantly boosted human productivity and convenience.
However, the increasing sophistication and autonomy of AI technologies have also introduced a variety of societal risks and ethical concerns. Among the most pressing of these are challenges related to AI safety and the alignment of AI behavior with human values. For instance, AI systems have been found to perpetuate bias in recruitment decisions, produce offensive or harmful content during interactions with users, and even pose existential threats in high-stakes domains such as autonomous weapons. These examples reflect growing anxieties about the potential misalignment between AI behavior and the ethical principles upheld by human societies. If left unaddressed, such misalignment could lead to consequences that undermine social trust and moral norms.
In response to these challenges, the concept of AI value alignment has emerged as a central concern within the broader field of AI safety research. AI value alignment refers to the development of AI systems whose goals, behaviors, and decision-making processes are consistent with the values, preferences, and ethical standards of individuals or society as a whole. Technically, several value alignment methodologies have been proposed, including reinforcement learning from human feedback (RLHF), inverse reinforcement learning (IRL), and constitutional AI. These approaches aim to incorporate normative constraints into the training process, thereby steering AI systems toward behavior that is both desirable and predictable. While promising in many respects, such methods face significant limitations. In particular, aligned AI systems often exhibit reduced adaptability when faced with novel scenarios and suffer from poor interpretability, making it difficult to trace or understand the reasoning behind their decisions. These limitations highlight the insufficiency of a purely engineering-driven approach and suggest the necessity of incorporating broader, interdisciplinary perspectives.
One promising approach is to integrate insights from psychology, the scientific study of human behavior, cognition, and moral reasoning, into the research and development of AI value alignment. Psychological theories provide robust conceptual tools for understanding how humans construct values, make moral judgments, and resolve ethical dilemmas in complex social contexts. Rather than designing AI systems that merely replicate the surface-level patterns of human behavior, these insights can inform architectures that embody internal mechanisms analogous to those involved in human moral cognition. Thus, true value alignment requires more than behavioral mimicry; it demands a form of cognitive and ethical compatibility between artificial agents and the human mind, particularly in terms of value judgment and moral decision-making processes.
This paper explores how psychological science can contribute to advancing AI value alignment. It reviews core psychological theories concerning the formation of moral values, dual-process models of moral reasoning, and the roles of emotion and social context in ethical decision-making. Building on these foundations, we propose conceptual frameworks that include the construction of a unified moral cognitive space capable of integrating diverse human values, and the development of dual-system moral architectures that emulate the interaction between intuitive and deliberative reasoning in human moral cognition. To ground these ideas in practice, we use altruistic behavior—a central and complex moral phenomenon—as a case study, examining how its psychological underpinnings could be modeled in AI systems to promote socially aligned decision-making.
By bridging AI safety research with psychological theory, this work seeks to support the development of more interpretable, robust, and ethically aware AI systems. Such interdisciplinary integration is not only timely, but also essential to ensure that the evolution of AI technologies remains aligned with the fundamental values of human society.

Key words

artificial intelligence / AI value alignment / value alignment / theory of mind / moral decision-making / altruism

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Jin Shaoxiong, Liu Chao. From Human Mind to Artificial Intelligence: Advancing AI Value Alignment Through Psychological Theories[J]. Journal of Psychological Science. 2025, 48(4): 782-791 https://doi.org/10.16719/j.cnki.1671-6981.20250402

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