Explainable Artificial Intelligence (XAI) techniques are frequently required by users in many AI systems with the goal of understanding complex models, their associated predictions, and gaining trust. While suitable for some specific tasks during development, their adoption by organisations to enhance trust in machine learning systems has unintended consequences. In this paper we discuss XAI's limitations in deployment and conclude that transparency alongside with rigorous validation are better suited to gaining trust in AI systems.

翻译：可解释人工智能（XAI）技术在许多AI系统中被用户频繁要求使用，其目标是理解复杂模型、相关预测并建立信任。尽管这些技术适用于开发阶段的特定任务，但组织采用它们来增强对机器学习系统的信任却产生了意想不到的后果。本文讨论了XAI在部署中的局限性，并得出结论：透明度与严格验证相结合，更适合建立对AI系统的信任。