AI explanations are often mentioned as a way to improve human-AI decision-making, but 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 -- beliefs or heuristics, based on prior knowledge, experience, or pattern recognition, used to make judgments -- 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：人工智能解释常被提及作为改善人机决策的一种方式，但实证研究尚未发现解释有效性的 一致证据，反而表明它们可能在AI系统出错时增加过度依赖。虽然影响对AI支持依赖的因素很多，但一个重要因素是决策者如何协调自身直觉（基于先验知识、经验或模式识别形成的用于判断的信念或启发式）与AI系统提供的信息，以决定何时推翻AI预测。我们针对两种预测任务开展了有声思维混合方法研究，涉及两种解释类型（基于特征和基于示例），探究决策者的直觉如何影响其对AI预测和解释的使用，并最终影响其选择何时依赖AI。我们的结果识别出三种参与AI预测和解释推理的直觉类型：关于任务结果、特征和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预测的三种观察路径。我们利用这些路径解释：(1)我们使用的基于特征的解释未能改善参与者的决策结果，反而增加了他们对AI的过度依赖；(2)我们使用的基于示例的解释比基于特征的解释更能提升决策者表现，并有助于实现人机互补性能。总体而言，我们的研究指出了AI决策支持系统和解释方法的进一步发展方向，帮助决策者有效运用直觉以实现对AI的适当依赖。