The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods. These methods seek to enhance the trust of end-users in automated systems by providing insights into the rationale behind their decisions. This paper presents a novel approach for measuring user trust in XAI systems, allowing their refinement. Our proposed metric combines both performance metrics and trust indicators from an objective perspective. To validate this novel methodology, we conducted a case study in a realistic medical scenario: the usage of XAI system for the detection of pneumonia from x-ray images.

翻译：随着深度学习模型日益广泛的应用及其固有的缺乏透明度的问题，催生了一个新的研究领域——可解释人工智能（XAI）方法。这些方法通过揭示模型决策背后的逻辑，旨在增强最终用户对自动化系统的信任。本文提出了一种新颖的用户对XAI系统信任度的测量方法，以实现系统的优化改进。我们提出的度量标准从客观角度结合了性能指标与信任指标。为验证这一新方法，我们在一个真实医疗场景中进行了案例研究：使用XAI系统从X光图像中检测肺炎。