Our work serves as a framework for unifying the challenges of contemporary explainable AI (XAI). We demonstrate that while XAI methods provide supplementary and potentially useful output for machine learning models, researchers and decision-makers should be mindful of their conceptual and technical limitations, which frequently result in these methods themselves becoming black boxes. We examine three XAI research avenues spanning image, textual, and graph data, covering saliency, attention, and graph-type explainers. Despite the varying contexts and timeframes of the mentioned cases, the same persistent roadblocks emerge, highlighting the need for a conceptual breakthrough in the field to address the challenge of compatibility between XAI methods and application tasks.

翻译：我们的工作旨在构建一个统一当代可解释人工智能（XAI）挑战的框架。研究表明，尽管XAI方法为机器学习模型提供了辅助性且可能具有价值的输出，但研究人员与决策者应警惕其概念与技术层面的局限性，这些局限性往往导致这些方法本身沦为黑箱。我们考察了涵盖图像、文本和图数据的三个XAI研究方向，涉及显著性、注意力及图类型解释器。尽管上述案例背景与时间跨度各异，但相同的关键障碍反复出现，凸显了该领域亟需概念突破以应对XAI方法与实际应用任务兼容性的挑战。