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.

在需求工程领域中，可解释人工智能（XAI）在使AI支持系统与用户需求、社会期望和监管标准相一致方面的日益重要性已获得认可。简而言之，可解释性已经成为影响系统质量的重要非功能性要求。本文在批判性地研究所谓的权衡问题上提出了观点。我们认为最好以细致入微的方式来处理这个问题，包括资源可用性、领域特性和风险考虑。通过为未来的研究和最佳实践奠定基础，本工作旨在推动AI领域中的需求工程。

重新审视解释型人工智能（XAI）中的性能-可解释性权衡