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Published in:
A Novel Bayes’ Theorem for Upper Probabilities Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Semantic Attribution for Explainable Uncertainty Quantification

Authors : Hanjing Wang, Shiqiang Wang, Qiang Ji

Published in: Epistemic Uncertainty in Artificial Intelligence

Publisher: Springer Nature Switzerland

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Abstract

Bayesian deep learning, with an emphasis on uncertainty quantification, is receiving growing interest in building reliable models. Nonetheless, interpreting and explaining the origins and reasons for uncertainty presents a significant challenge. In this paper, we present semantic uncertainty attribution as a tool for pinpointing the primary factors contributing to uncertainty. This approach allows us to explain why a particular image carries high uncertainty, thereby making our models more interpretable. Specifically, we utilize the variational autoencoder to disentangle different semantic factors within the latent space and link the uncertainty to corresponding semantic factors for an explanation. The proposed techniques can also enhance explainable out-of-distribution (OOD) detection. We can not only identify OOD samples via their uncertainty, but also provide reasoning rooted in a semantic concept.

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previous chapter Towards Offline Reinforcement Learning with Pessimistic Value Priors
Footnotes
1
There is debate over whether deep ensemble is a Bayesian method. We believe it is since each ensemble component can serve as a mode of \(p(\theta |\mathcal {D})\).
 
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Metadata
Title
Semantic Attribution for Explainable Uncertainty Quantification
Authors
Hanjing Wang
Shiqiang Wang
Qiang Ji
Copyright Year
2024
Book
Epistemic Uncertainty in Artificial Intelligence

Print ISBN: 978-3-031-57962-2

Electronic ISBN: 978-3-031-57963-9

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-57963-9_8

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