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

TL;DR

This paper critically examines the supposed trade-off between explainability and performance in XAI, arguing for a nuanced approach that considers resources, domain specifics, and risk, to better guide requirements engineering.

Contribution

It challenges the traditional view of a strict trade-off in XAI, proposing a more nuanced understanding that incorporates multiple contextual factors.

Findings

The trade-off is not absolute but context-dependent.

Resource availability influences explainability-performance balance.

A nuanced approach can improve requirements engineering for AI systems.

Abstract

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.