用假设驱动替代推荐，但实验显示效果有限。
An Empirical Examination of the Evaluative AI Framework
- 不给直接建议，而是呈现正反证据支持假设决策
- 实验中决策表现未显著提升，用户对证据参与度低
- 适合对决策可解释性有要求的研究者进一步探索
本研究实证检验了旨在通过从推荐式转向假设驱动来提升用户决策质量的「评估型AI」框架。该框架不直接提供推荐，而是向用户提供支持或反对特定假设的正反证据，以促进更知情的决策。然而，当前行为实验结果显示，该框架并未显著改善决策表现，且用户对所提证据的参与度有限，认知过程与传统AI系统相似。尽管如此，该框架仍具有进一步研究的潜力。
原文摘要 · Abstract (English)
This study empirically examines the "Evaluative AI" framework, which aims to enhance the decision-making process for AI users by transitioning from a recommendation-based approach to a hypothesis-driven one. Rather than offering direct recommendations, this framework presents users pro and con evidence for hypotheses to support more informed decisions. However, findings from the current behavioral experiment reveal no significant improvement in decision-making performance and limited user engagement with the evidence provided, resulting in cognitive processes similar to those observed in traditional AI systems. Despite these results, the framework still holds promise for further exploration in future research.
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