Who is More Trustworthy, AI or Humans? Modeling Human-AI Interactions in Repeated Trust Games with Reinforcement Learning

Tan Haotian; Li Zeqing; Wu Zhen

doi:10.16719/j.cnki.1671-6981.20250413

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Journal of Psychological Science ›› 2025, Vol. 48 ›› Issue (4) : 920-932. DOI: 10.16719/j.cnki.1671-6981.20250413

Computational modeling and artificial intelligence

Who is More Trustworthy, AI or Humans? Modeling Human-AI Interactions in Repeated Trust Games with Reinforcement Learning

Tan Haotian¹, Li Zeqing², Wu Zhen^1,3

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Abstract

With the explosive advancement of artificial intelligence (AI), human society is entering a new era of “Human-AI Interaction.” The chatbots we interact with, the algorithmic recommendations embedded in software, and the autonomous driving systems in vehicles all rely on AI algorithms. Moreover, AI has become increasingly integrated into critical fields such as education, healthcare, and finance. Undeniably, it has become an indispensable component of modern life. In this context, understanding trust in Human-AI Interaction not only contributes to the development of prosocial AI but also holds significant value for promoting human-AI collaboration and advancing social harmony.
As one of the core mechanisms of human social interaction, trust is defined as the willingness to hold positive expectations of others’ behaviors and expose oneself to potential risks when unable to control the actions of others. AI is reshaping how people interact with their environments, and trust has now extended from human-human relationships to human-AI interactions. Existing research has primarily used self-reported questionnaires or one-shot interactions to identify key factors influencing human trust in AI. However, these “snapshot” studies provide limited insight into the dynamic development of trust. As seen in human-human interactions, trust typically develops over repeated encounters. The mechanisms underlying trust formation in human-AI interactions remain unclear.
To bridge these gaps, the present study aimed to compare the similarities and differences between Human-AI trust and Human-Human trust using a repeated trust game paradigm. It also sought to use reinforcement learning model to explore the computational mechanisms underlying the development of trust in Human-AI interactions. A total of 148 participants were recruited and completed the experiment. During the task, participants engaged in 20 rounds of trust games with each of 6 different trustees—either human or AI. Each trustee’s reciprocal behavior was governed by a predetermined return probability (25%, 50%, or 75%), which was randomly determined by the program. Trustees were distinguished by different images and labels. The order of trustees was counterbalanced within participants using a Latin square design. To enhance ecological validity, the return probabilities were anonymized.
The results showed that, at the beginning of the trust game, participants did not exhibit differential investment levels toward human or AI in all conditions. However, over repeated interactions, participants displayed higher investment probabilities when interacting with AI compared to human. Moreover, participants were less likely to stop investing after experiencing a loss when interacting with AI, indicating fewer loss-shift behaviors. In contrast, after successful investments, participants’ behavior did not differ across trustee types.
A hyperbolic model revealed an interaction between trustee type and return probability. While return probabilities did not significantly affect the temporal trajectory of investment behavior toward AI, they did influence behavior in human interactions. Specifically, lower return probabilities led to a steeper decline in investment rates toward human trustees. Moreover, participants exhibited a higher asymptotic level of investment when interacting with AI, regardless of return probability, suggesting that if the trust game were to continue indefinitely, the probability of investing in AI would remain higher.
Further, the results of the reinforcement learning model indicated that the four-parameter model incorporating trustee type provided the best fit. The results showed that participants exhibited lower negative feedback learning rates and higher temperature parameters when interacting with AI, suggesting reduced sensitivity to negative outcomes and increased exploratory behavior. There were no significant differences in positive feedback learning rates or initial expected utility between human and AI trustees.
Taken together, these findings suggest that, although there is no initial trust bias toward humans or AI, participants gradually develop greater trust in AI over repeated interactions. Moreover, participants exhibit greater tolerance for non-cooperative behaviors displayed by AI. Trust in human-AI interactions appears to be more resilient. These results provide the first evidence of differences in the trust formation processes between human-human and human-AI interactions and offer a foundation for understanding the cognitive adaptation processes elicited by AI in social interactions.

Key words

human-AI interaction / repeated trust game / reinforcement learning

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Tan Haotian, Li Zeqing, Wu Zhen. Who is More Trustworthy, AI or Humans? Modeling Human-AI Interactions in Repeated Trust Games with Reinforcement Learning[J]. Journal of Psychological Science. 2025, 48(4): 920-932 https://doi.org/10.16719/j.cnki.1671-6981.20250413

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