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.

翻译：本研究对"评估性人工智能"框架进行了实证检验，该框架旨在通过从基于推荐的方法转变为假设驱动的方法，以增强人工智能用户的决策过程。该框架不提供直接推荐，而是向用户呈现支持与反对假设的证据，以支持更明智的决策。然而，当前行为实验的结果显示，决策绩效并未得到显著改善，用户对所提供证据的参与度有限，导致认知过程与传统人工智能系统中观察到的相似。尽管存在这些结果，该框架在未来研究中仍具有进一步探索的价值。