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.

翻译：在需求工程（RE）领域，可解释人工智能（XAI）在使AI支持系统与用户需求、社会期望及监管标准保持一致方面的重要性日益凸显，已获得广泛认可。一般而言，可解释性已成为影响系统质量的重要非功能性需求。然而，可解释性与性能之间所谓的权衡关系对可解释性假定的积极影响提出了挑战。如果满足可解释性要求意味着系统性能下降，则必须审慎考虑这两个质量方面孰先孰后，以及如何在两者之间达成妥协。在本文中，我们批判性地审视了这种所谓的权衡关系。我们认为，最佳应对方式应结合资源可用性、领域特征及风险考量，采取细致入微的方法。通过为未来研究和最佳实践奠定基础，本工作旨在推动面向AI的需求工程领域发展。