arXiv:2605.23867v1 Announce Type: cross Abstract: Large language models (LLMs) have the potential to aid and improve human decision-making in classification tasks, not only by providing fairly accurate predictions, but also in their ability to generate cogent narrative explanations of those predictions. Prior work has demonstrated that people generally find AI narrative explanations to be understandable, trustworthy, and convincing for changing beliefs and opinions; however, less is known about the impact of narrative explanations on objective human decision-making performance. Here we conduct
The proliferation of advanced LLMs capable of generating coherent explanations necessitates a deeper understanding of their influence on human decision-making beyond simple predictions.
Understanding how persuasive LLM explanations impact human decisions is crucial for designing effective and ethical AI systems, particularly in critical classification tasks.
The focus shifts from merely LLM prediction accuracy to the quality and persuasive impact of LLM-generated explanations on human cognitive processes and ultimate outcomes.
- · AI developers focused on explainable AI (XAI)
- · Consulting firms leveraging AI for decision support
- · Industries requiring complex classification (e.g., medical, legal)
- · Developers offering only opaque 'black box' AI solutions
- · Organizations failing to integrate human-centered AI design
- · Systems where explainability is not prioritized
Increased demand for LLMs capable of generating nuanced and persuasive explanations tailored for human understanding.
Development of new metrics and frameworks to objectively evaluate the 'persuasiveness' and 'decision-making impact' of AI explanations.
Ethical and regulatory debates intensify around the potential for manipulative or unduly persuasive AI explanations in sensitive domains.
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