Trust in AI is dynamically updated based on users' expectations

Human reliance on artificial intelligence (AI) advice in decision-making varies, with both over- and under-reliance observed. Timing of AI advice has been proposed to address these biases. Additionally, trust in the AI also influences reliance. In a deepfake detection task, we investigated how AI advice affects human performance and decision-making. Using a large online participant pool, we compared task performance when AI advice was provided either concurrently with decisions or after an initial evaluation. We found that while AI advice improved deepfake detection, the timing of advice did not affect performance. Instead, we revealed, using computational modelling, that trust in the AI dynamically influenced participants' willingness to agree with its advice, based on expectations of its ability. These findings highlight the importance of developing appropriate trust in AI-powered decision support systems for effective human-AI collaboration, with applications in designing and testing decision-support tools for perceptual tasks.