Despite explosive growth in Explainable AI (XAI) research—spanning feature attribution methods, sparse autoencoders, and attention visualization—a new position paper challenges whether these techniques actually matter in production environments. The research argues that explanations are frequently generated and discarded without meaningfully guiding organizational workflows or influencing consequential decisions. This disconnect between academic output and practical utility represents a fundamental failure in how the field has evolved. Rather than building robust theoretical foundations for interpretability, researchers have pursued an ad-hoc patchwork of techniques optimized for publishability rather than real-world impact. The paper signals growing frustration within the community that explainability research has become disconnected from the actual problem it claims to solve: making AI systems trustworthy and actionable for practitioners who bear responsibility for model deployment.
The core argument centers on a missing foundational framework. Current XAI approaches treat explainability as a technical problem—how to extract feature importance or visualize decision boundaries—rather than addressing structural questions about what stakeholders actually need to know and when they need to know it. In healthcare contexts, for instance, clinicians require explanations formatted for rapid triage decisions, yet many XAI methods produce outputs designed for model developers. Similarly, regulatory compliance demands traceable audit trails, not post-hoc attribution scores. The position paper emphasizes that without first establishing what explanation means in specific organizational contexts, researchers continue building irrelevant tools. This requires moving beyond metrics like faithfulness or completeness that measure technical properties divorced from human utility.
The paper advocates for a structural rethinking where XAI research begins with stakeholder needs and institutional constraints, then works backward to determine which explanation methods genuinely serve those requirements. This foundational shift demands collaboration between computer scientists, domain experts, and organizational decision-makers before technique development begins. Rather than assuming all stakeholders need identical explanations, this approach recognizes that a loan officer, regulator, and data scientist require fundamentally different information from the same model. The position ultimately calls for treating explainability as an empirical problem about human-AI interaction rather than an algorithmic challenge, fundamentally redirecting how the field allocates research effort and measures success.