能力的错觉：评估解释对用户视觉问答系统心智模型的影响
The Illusion of Competence: Evaluating the Effect of Explanations on Users' Mental Models of Visual Question Answering Systems
AI总结：
本研究通过视觉问答任务探究解释对用户感知AI系统局限性的影响，发现解释非但未提升透明度，反而普遍增强用户对系统能力的感知，形成“能力错觉”。
摘要：
我们研究了当人工智能系统遇到无法完美完成的任务时，用户如何感知其局限性，以及在其答案旁边提供解释是否有助于用户构建关于系统能力和局限性的适当心智模型。我们采用了一项视觉问答和解释任务，通过操纵视觉输入来控制人工智能系统的局限性：在推理过程中，系统要么处理全彩色图像，要么处理灰度图像。我们的目标是确定参与者能否感知系统的局限性。我们假设解释将使有限的人工智能能力对用户更加透明。然而，我们的结果表明，解释并不具有这种效果。解释并没有让用户更准确地评估人工智能系统的局限性，而是普遍增加了用户对系统能力的感知——无论其实际表现如何。
英文摘要：
We examine how users perceive the limitations of an AI system when it encounters a task that it cannot perform perfectly and whether providing explanations alongside its answers aids users in constructing an appropriate mental model of the system's capabilities and limitations. We employ a visual question answer and explanation task where we control the AI system's limitations by manipulating the visual inputs: during inference, the system either processes full-color or grayscale images. Our goal is to determine whether participants can perceive the limitations of the system. We hypothesize that explanations will make limited AI capabilities more transparent to users. However, our results show that explanations do not have this effect. Instead of allowing users to more accurately assess the limitations of the AI system, explanations generally increase users' perceptions of the system's competence - regardless of its actual performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
