对依赖可解释人工智能（XAI）的建模：表明其目的与注意力
Modeling Reliance on XAI Indicating Its Purpose and Attention
AI总结：
本研究通过两个实验与结构方程模型分析，探究展示目的与注意力热图的XAI对人类信任、依赖及合规行为的影响，明确了不同使用场景与任务难度下的作用差异。
摘要：
本研究采用可展示自身目的与注意力作为过程解释的XAI（可解释人工智能），探究这类解释如何影响人类对AI的信任与使用。研究生成了指示AI注意力的热图，开展实验1以验证热图可解释性的有效性，开展实验2以从依赖（依靠AI）与合规（接受AI答案）两个维度，探究目的说明与热图的影响。结构方程模型（SEM）分析结果显示：（1）展示AI目的对信任的正向与负向影响，取决于AI使用类型（依赖或合规）以及任务难度；（2）仅展示热图会在难度更高的任务中对信任产生负面影响；（3）在难度更高的任务中，热图可凭借其可解释性对信任产生正向影响。
英文摘要：
This study used XAI, which shows its purposes and attention as explanations of its process, and investigated how these explanations affect human trust in and use of AI. In this study, we generated heat maps indicating AI attention, conducted Experiment 1 to confirm the validity of the interpretability of the heat maps, and conducted Experiment 2 to investigate the effects of the purpose and heat maps in terms of reliance (depending on AI) and compliance (accepting answers of AI). The results of structural equation modeling (SEM) analyses showed that (1) displaying the purpose of AI positively and negatively influenced trust depending on the types of AI usage, reliance or compliance, and task difficulty, (2) just displaying the heat maps negatively influenced trust in a more difficult task, and (3) the heat maps positively influenced trust according to their interpretability in a more difficult task.
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。
