AI explanations are often mentioned as a way to improve human-AI decision-making. Yet, empirical studies have not found consistent evidence of explanations' effectiveness and, on the contrary, suggest that they can increase overreliance when the AI system is wrong. While many factors may affect reliance on AI support, one important factor is how decision-makers reconcile their own intuition -- which may be based on domain knowledge, prior task experience, or pattern recognition -- with the information provided by the AI system to determine when to override AI predictions. We conduct a think-aloud, mixed-methods study with two explanation types (feature- and example-based) for two prediction tasks to explore how decision-makers' intuition affects their use of AI predictions and explanations, and ultimately their choice of when to rely on AI. Our results identify three types of intuition involved in reasoning about AI predictions and explanations: intuition about the task outcome, features, and AI limitations. Building on these, we summarize three observed pathways for decision-makers to apply their own intuition and override AI predictions. We use these pathways to explain why (1) the feature-based explanations we used did not improve participants' decision outcomes and increased their overreliance on AI, and (2) the example-based explanations we used improved decision-makers' performance over feature-based explanations and helped achieve complementary human-AI performance. Overall, our work identifies directions for further development of AI decision-support systems and explanation methods that help decision-makers effectively apply their intuition to achieve appropriate reliance on AI.

翻译：AI解释常被提及为改善人机协作决策的一种方式。然而，实证研究尚未发现解释有效性的统一证据，反而表明在AI系统出错时，解释可能增加过度依赖。尽管多种因素可能影响对AI支持的依赖，但一个重要因素是决策者如何协调自身直觉（可能基于领域知识、先前任务经验或模式识别）与AI系统提供的信息，以确定何时推翻AI预测。我们针对两项预测任务，采用两种解释类型（基于特征和基于示例）进行有声思维混合方法研究，探索决策者直觉如何影响其对AI预测和解释的使用，并最终影响其选择何时依赖AI。研究结果确定了在推理AI预测和解释时涉及的三类直觉：关于任务结果的直觉、关于特征的直觉以及关于AI局限性的直觉。基于此，我们总结了决策者应用自身直觉并推翻AI预测的三条观察路径。我们利用这些路径解释：（1）我们使用的基于特征的解释为何未能改善参与者的决策结果，反而加剧了他们对AI的过度依赖；（2）我们使用的基于示例的解释相比基于特征的解释如何提升决策者表现，并实现人机互补性能。总体而言，本研究为开发能帮助决策者有效运用直觉以实现对AI适度依赖的AI决策支持系统与解释方法指明了方向。