The artificial intelligence research community is facing a reckoning. Despite an explosion of explainability techniques—from feature attribution methods to sparse autoencoders—a new position paper argues that most explanations never meaningfully influence real-world workflows. Instead, they are generated, examined, and discarded without guiding any consequential decisions. This critique comes as machine learning systems increasingly make high-stakes determinations in healthcare, finance, and criminal justice, where understanding model reasoning has become critical for regulatory compliance and public trust. The paper's core claim cuts to the heart of a field that has published thousands of papers on interpretability without establishing whether any of them actually work in practice.
The explainability research community has optimized for novelty and academic citation rather than solving the core problem: making explanations that practitioners can actually use. Most XAI papers present techniques in isolation, tested on benchmark datasets with little consideration for how explanations function within existing organizational workflows or technical infrastructure. When companies deploy machine learning models, they face concrete constraints—time pressure, domain expertise gaps, and integration challenges—that academic explainability research rarely addresses. A radiologist cannot wait five minutes for an explanation of why a model flagged a suspicious lesion, nor can a loan officer effectively challenge a credit denial based on abstract feature importance scores without business context. This gap between what researchers build and what practitioners need has created a cycle where new explainability methods generate papers but fail to improve actual decision-making processes.
The position paper calls for explainability research to shift from ad-hoc method development toward foundational work on how explanations integrate with human cognition and organizational decision structures. This represents a fundamental reorientation: rather than asking 'how can we explain this model,' researchers should ask 'what explanation format will cause a specific person to make a better decision in a specific context.' This requires interdisciplinary collaboration with cognitive scientists, organizational behaviorists, and domain experts—not just computer scientists. Several recent papers on hierarchical learning architectures and multi-agent systems suggest one promising direction: building systems where explanations are generated at multiple levels of abstraction, allowing different stakeholders to access appropriate detail. Until the field grapples with this implementation gap, explainable AI risks becoming sophisticated window-dressing on opaque systems rather than a genuine tool for accountability.