Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)

Title data

Crook, Barnaby ; Schlüter, Maximilian ; Speith, Timo:
Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI).
In: Schneider, Kurt ; Dalpiaz, Fabiano ; Horkoff, Jennifer (ed.): 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW). - Piscataway, NJ : IEEE , 2023 . - pp. 316-324
ISBN 979-8-3503-2691-8
DOI: https://doi.org/10.1109/REW57809.2023.00060

Project information

Abstract in another language

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.