  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.
