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.

本文提出了一个框架来统一解释人工智能（XAI）领域的挑战。尽管 XAI 方法可以为机器学习模型提供有用的输出，但研究人员和决策者需要注意这些方法的概念和技术限制，这些限制通常导致这些方法本身变成黑匣子。作者使用图像、文本和图形数据，研究了涉及突出显示、关注和图形解释器的三种 XAI 研究途径。尽管这些案例的背景和时间跨度各不相同，但是同样的持续困难仍然出现，这凸显了在此领域要应对 XAI 方法和应用任务之间兼容性挑战的概念突破的必要性。