Explainable Artificial Intelligence (XAI) is essential for building advanced machine learning-powered applications, especially in critical domains such as medical diagnostics or autonomous driving. Legal, business, and ethical requirements motivate using effective XAI, but the increasing number of different methods makes it challenging to pick the right ones. Further, as explanations are highly context-dependent, measuring the effectiveness of XAI methods without users can only reveal a limited amount of information, excluding human factors such as the ability to understand it. We propose to evaluate XAI methods via the user's ability to successfully perform a proxy task, designed such that a good performance is an indicator for the explanation to provide helpful information. In other words, we address the helpfulness of XAI for human decision-making. Further, a user study on state-of-the-art methods was conducted, showing differences in their ability to generate trust and skepticism and the ability to judge the rightfulness of an AI decision correctly. Based on the results, we highly recommend using and extending this approach for more objective-based human-centered user studies to measure XAI performance in an end-to-end fashion.

本研究解决了选择适当的可解释人工智能（XAI）方法的问题，以满足法律、商业和伦理要求。通过设计代理任务来评估用户执行成功的能力，我们发现不同的XAI方法在生成信任和怀疑方面存在差异，并影响人类决策的有效性。这项研究的结果强调了使用基于目标的用户中心研究来测量XAI性能的重要性。

可解释人工智能的有效性研究