Transparent AI: The Case for Interpretability and Explainability
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Abstract (arXiv)
As artificial intelligence systems increasingly inform high-stakes decisions across sectors, transparency has become foundational to responsible and trustworthy AI implementation. Leveraging our role as a leading institute in advancing AI research and enabling industry adoption, we present key insights and lessons learned from practical interpretability applications across diverse domains. This paper offers actionable strategies and implementation guidance tailored to organizations at varying stages of AI maturity, emphasizing the integration of interpretability as a core design principle rather than a retrospective add-on.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 31 Jul 2025.
Builds on
10 of 15 references.
References · 10
- 2018Sanity Checks for Saliency Mapscited by 1,156
- 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 419
- 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
- 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 302
- 2022Evaluation of post-hoc interpretability methods in time-series classificationcited by 53
- 2024From Understanding to Utilization: A Survey on Explainability for Large Language Modelscited by 48
- 2022Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysiscited by 35
- 2023Is Ignorance Bliss? The Role of Post Hoc Explanation Faithfulness and Alignment in Model Trust in Laypeople and Domain Expertscited by 7
- 2024A Comprehensive Guide to Explainable AI: From Classical Models to LLMscited by 7
- 2024Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Datacited by 2
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.