Explainable AI (XAI)

Human Decision-Making with Persuasive and Narrative LLM Explanations

A large-scale behavioral experiment evaluated how LLM-generated narrative explanations of varying persuasiveness affect human decision-making accuracy in classification tasks. Results showed that persuasiveness level did not meaningfully improve decision accuracy over a simple AI prediction alone, consistent with prior explainable AI research using feature importance methods. Narratives increased AI reliance regardless of whether the AI prediction was correct or incorrect, and more persuasive narratives may have slowed response times and reduced ability to discriminate correct from incorrect AI predictions. The study concludes that narrative explanations involve tradeoffs and warrant further investigation into when and how they should be deployed.