mlsanityData to 14 Oct 2026

Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustability

arXiv 2105.11828 ↗First posted 25 May 2021cs.AI cs.CVcited by 11

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Abstract (arXiv)

After the tremendous advances of deep learning and other AI methods, more attention is flowing into other properties of modern approaches, such as interpretability, fairness, etc. combined in frameworks like Responsible AI. Two research directions, namely Explainable AI and Uncertainty Quantification are becoming more and more important, but have been so far never combined and jointly explored. In this paper, I show how both research areas provide potential for combination, why more research should be done in this direction and how this would lead to an increase in trustability in AI systems.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 25 May 2021.

Builds on

10 of 23 references.

References · 10

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  4. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,319
  5. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
  6. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  7. 2019Your Classifier is Secretly an Energy Based Model and You Should Treat it Like Onecited by 405
  8. 2019On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networkscited by 305
  9. 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
  10. 2019Uncertainty Quantification with Generative Modelscited by 17

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Cited by year

Citing papers per year: 2022: 2; 2023: 4; 2024: 0; 2025: 5.

11 papers.

  1. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 125
  2. 2023Machine learning with data assimilation and uncertainty quantification for dynamical systems: a reviewcited by 75
  3. 2022Explainable AI for High Energy Physicscited by 7
  4. 2022A Detailed Study of Interpretability of Deep Neural Network based Top Taggerscited by 7
  5. 2025Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networkscited by 3
  6. 2025Uncertainty Estimation by Human Perception versus Neural Modelscited by 3
  7. 2025Uncertainty-Aware Explainable Federated Learningcited by 2
  8. 2025Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantificationcited by 1
  9. 2023Probabilistic computation and uncertainty quantification with emerging covariancecited by 0
  10. 2023Uncertainty Quantification using Generative Approachcited by 0
  11. 2025Uncertainty Awareness and Trust in Explainable AI- On Trust Calibration using Local and Global Explanationscited by 0