  Within the field of Requirements Engineering (RE), the increasing
significance of Explainable Artificial Intelligence (XAI) in aligning
AI-supported systems with user needs, societal expectations, and regulatory
standards has garnered recognition. In general, explainability has emerged as
an important non-functional requirement that impacts system quality. However,
the supposed trade-off between explainability and performance challenges the
presumed positive influence of explainability. If meeting the requirement of
explainability entails a reduction in system performance, then careful
consideration must be given to which of these quality aspects takes precedence
and how to compromise between them. In this paper, we critically examine the
alleged trade-off. We argue that it is best approached in a nuanced way that
incorporates resource availability, domain characteristics, and considerations
of risk. By providing a foundation for future research and best practices, this
work aims to advance the field of RE for AI.
