Getting a CLUE: A Method for Explaining Uncertainty Estimates
Open accessAbstract
Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems. However, there is little work at the intersection of these two areas. We address this gap by proposing a novel method for interpreting uncertainty estimates from differentiable probabilistic models, like Bayesian Neural Networks (BNNs). Our method, Counterfactual Latent Uncertainty Explanations (CLUE), indicates how to change an input, while keeping it on the data manifold, such that a BNN becomes more confident about the input's prediction. We validate CLUE through 1) a novel framework for evaluating counterfactual explanations of uncertainty, 2) a series of ablation experiments, and 3) a user study. Our experiments show that CLUE outperforms baselines and enables practitioners to better understand which input patterns are responsible for predictive uncertainty.
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CLUE is a method to help identify which input features are responsible for models' uncertainty.
“Recently, Antoran et al. [2] introduced CLUE, a method to help identify which input features are responsible for models' uncertainty.”
From Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2021) · cites this paper as [2]
The practitioner could identify which subgroups are unequally represented between training and validation sets.
“The practitioner could then identify which subgroups are unequally represented between their training and validation sets [2].”
From Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2021) · cites this paper as [2]
Section 5.2 (p. 7) evaluates whether practitioners can identify sources of model uncertainty, and Appendix J.2 (p. 34) asks them to predict which test points will be uncertain. Neither passage addresses identifying subgroups unequally represented between training and validation sets; searches for those terms found no supporting passage. (LLM, conf=0.72)
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Self-citation
- Authors
- Antorán, J., Bhatt, U., Adel, T., Weller, A., & Hernández-Lobato, J. M.
- Affiliations
- University of Cambridge (GB)
- Venue
- Apollo - University of Cambridge Repository
- Publisher
- Apollo - University of Cambridge Repository
- Published
- 2021-05-18
- ISSN
- 2331-8422 (ISSN-L)
- OpenAlex
- W3132800470
- Cited by
- 49
- References
- 44
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 11 · 2023: 12 · 2022: 7 · 2021: 13 · 2020: 1
- Type
- posted content
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- arXiv (Cornell University) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- arXiv (Cornell University)
- Apollo
- International Conference on Learning Representations
- Funders
- Microsoft Research
- Alan Turing Institute (EP/N510129/1., N510129)
- DeepMind
- Leverhulme Trust
- Mozilla Foundation
- Engineering and Physical Sciences Research Council (N510129, EP/N510129/, EP/N510129/1, TU/B/000074, 2275741)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- uncertainty estimation, counterfactual explanations, Bayesian neural networks, interpretability, data manifold, trustworthy machine learning, explanation evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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