# Humans learn to prefer trustworthy AI over human partners

arXiv: https://arxiv.org/abs/2507.13524 · HTML: https://mlsanity.com/paper/2507.13524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial](https://mlsanity.com/paper/2403.14380/) (2024)
- [The Ethics of Advanced AI Assistants](https://mlsanity.com/paper/2404.16244/) (2024)
- [Machine Culture](https://mlsanity.com/paper/2311.11388/) (2023)
- [Do LLM Agents Exhibit Social Behavior?](https://mlsanity.com/paper/2312.15198/) (2023)
- [A new sociology of humans and machines](https://mlsanity.com/paper/2402.14410/) (2024)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM](https://mlsanity.com/paper/2409.07871/) (2024)
- [On the Implications of Verbose LLM Outputs: A Case Study in Translation Evaluation](https://mlsanity.com/paper/2410.00863/) (2024)

## Abstract (arXiv)

Partner selection is crucial for cooperation and hinges on communication. As artificial agents, especially those powered by large language models (LLMs), become more autonomous, intelligent, and persuasive, they compete with humans for partnerships. Yet little is known about how humans select between human and AI partners and adapt under AI-induced competition pressure. We constructed a communication-based partner selection game and examined the dynamics in hybrid mini-societies of humans and bots powered by a state-of-the-art LLM. Through three experiments (N = 975), we found that bots, though more prosocial than humans and linguistically distinguishable, were not selected preferentially when their identity was hidden. Instead, humans misattributed bots' behaviour to humans and vice versa. Disclosing bots' identity induced a dual effect: it reduced bots' initial chances of being selected but allowed them to gradually outcompete humans by facilitating human learning about the behaviour of each partner type. These findings show how AI can reshape social interaction in mixed societies and inform the design of more effective and cooperative hybrid systems.