Charles Crabtree, John Holbein, Mitchell Bosley, Semra Sevi
Personalized interpersonal conversations are among the most effective known tools for reducing prejudice, yet they are difficult to scale because they require skilled human facilitators. This study tests whether AI can approximate the effects of these interventions. Using OpenAI's GPT-4o, we developed a messaging-based intervention that engaged US participants in individualized, morally aligned dialogs about transgender rights. In a preregistered experiment, these AI-mediated conversations significantly increased support for transgender rights across multiple attitudinal measures. Robustness checks, including weighting and sensitivity analyses, confirmed the reliability of these effects, and analyses of the conversations support the idea that moral matching between the AI and participants played a key role in reducing prejudice. However, follow-up data collected 1 week later indicated that the attitudinal gains diminished over time, suggesting that reinforcement may be necessary to sustain change. Together, these findings indicate that generative AI can facilitate value-aligned dialog capable of shifting social attitudes, while highlighting the challenge of achieving meaningful durable impacts.