[2604.03237] The Persuasion Paradox: When LLM Explanations Fail to Improve Human-AI Team Performance

Computer Science > Human-Computer Interaction arXiv:2604.03237 (cs) [Submitted on 31 Jan 2026] Title:The Persuasion Paradox: When LLM Explanations Fail to Improve Human-AI Team Performance Authors:Ruth Cohen, Lu Feng, Ayala Bloch, Sarit Kraus View a PDF of the paper titled The Persuasion Paradox: When LLM Explanations Fail to Improve Human-AI Team Performance, by Ruth Cohen and 2 other authors View PDF HTML (experimental) Abstract:While natural-language explanations from large language models (LLMs) are widely adopted to improve transparency and trust, their impact on objective human-AI team performance remains poorly understood. We identify a Persuasion Paradox: fluent explanations systematically increase user confidence and reliance on AI without reliably improving, and in some cases undermining, task accuracy. Across three controlled human-subject studies spanning abstract visual reasoning (RAVEN matrices) and deductive logical reasoning (LSAT problems), we disentangle the effects of AI predictions and explanations using a multi-stage reveal design and between-subjects comparisons. In visual reasoning, LLM explanations increase confidence but do not improve accuracy beyond the AI prediction alone, and substantially suppress users' ability to recover from model errors. Interfaces exposing model uncertainty via predicted probabilities, as well as a selective automation policy that defers uncertain cases to humans, achieve significantly higher accuracy and error recovery t...