Explainability Is Not a Feature: A Position on Trustworthy AI

Explainable Artificial Intelligence (XAI) is widely invoked as a central component of trustworthy AI. Despite this prominence, the concept of explainability remains deeply contested, with persistent disagreement regarding what constitutes an explanation, whom explanations are for, and the conditions under which they are meaningful. In this position paper, we argue that these disagreements reflect a fundamental limitation of current approaches that treat explainability as an intrinsic technical property of models. Drawing on a systematic synthesis of the XAI literature, we contend that explainability is more appropriately understood as a relational and context-dependent requirement, shaped by human roles, decision contexts, and risk profiles. We further argue that indiscriminate or exhaustive explanations can undermine trust, motivating the need for selective and purpose-driven explanatory practices. Finally, we discuss the implications of this reframing for trustworthy AI research, deployment, and regulatory oversight.