Is Task-Agnostic Explainable AI a Myth?

One-size-fits-all explainability doesn't exist: The paper demonstrates that XAI methods cannot be universally applied across different tasks and data types (images, text, graphs). Decision-makers should expect that an explanation method effective for one application may not work well for another, requiring task-specific evaluation rather than assuming transferability.

Explanation methods can become their own black boxes: Despite being designed to clarify how AI models work, XAI methods themselves often lack transparency and become difficult to interpret. This creates a problematic situation where the tool meant to explain the model becomes equally opaque, potentially misleading users about their understanding of the system.

Current XAI approaches have fundamental limitations: The paper identifies persistent technical and conceptual roadblocks across multiple XAI research areas (saliency maps, attention mechanisms, graph explainers). These aren't minor issues but suggest the field needs major rethinking rather than incremental improvements to existing methods.

XAI outputs should be treated as supplementary, not definitive: The authors caution that XAI methods provide "potentially useful" but limited output. Organizations deploying AI systems should not rely solely on these explanations for critical decisions and should maintain healthy skepticism about what explanations actually reveal about model behavior.

Compatibility between methods and real-world tasks remains unsolved: A key finding is that matching XAI methods to specific application requirements remains a significant unresolved challenge, suggesting organizations need domain expertise to properly validate whether an explanation method is actually appropriate for their use case.