A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)
This work addresses the challenge of balancing explainability and performance in AI for requirements engineering, but it is incremental as it critiques existing assumptions without introducing new methods or data.
The paper tackles the alleged trade-off between explainability and performance in AI systems, arguing for a nuanced approach that considers resource availability, domain characteristics, and risk, without providing concrete numerical results.
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.