mlsanityData to 7 Oct 2026

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

arXiv 2307.14239 ↗First posted 26 Jul 2023cs.AI cs.SEcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 30 of its 84 references that are in the map. First posted on arXiv 26 Jul 2023.

Builds on

30 of 84 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 1; 2026: 3.

10 papers.

  1. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  2. 2024From Frege to chatGPT: Compositionality in language, cognition, and deep neural networkscited by 7
  3. 2024T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformerscited by 4
  4. 2025Towards Transparent AI: A Survey on Explainable Language Modelscited by 3
  5. 2024Need of AI in Modern Education: in the Eyes of Explainable AI (xAI)cited by 1
  6. 2026Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solutioncited by 1
  7. 2024Distributed Lag Transformer based on Time-Variable-Aware Learning for Explainable Multivariate Time Series Forecastingcited by 0
  8. 2024Learning local discrete features in explainable-by-design convolutional neural networkscited by 0
  9. 2026Explainable Fall Detection for Elderly Monitoring via Temporally Stable SHAP in Skeleton-Based Human Activity Recognitioncited by 0
  10. 2026Verifying the Linear Representation Hypothesis: How Interpretable Are Vision SAEs?cited by 0