Papers › Getting a CLUE: A Method for Explaining Uncertainty Estimates

Getting a CLUE: A Method for Explaining Uncertainty Estimates

11 Jun 2020ICLR 2021 1arXiv:2006.06848archive 2025-07-28

Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato

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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X_dims_to_input_dim_vec cambridge-mlg/CLUE/src/compas_loader.py found in paper text by Syntology unverified MIT (permissive) · c0a6c860eb0a4920 · report
check_data_file cambridge-mlg/CLUE/src/LSAT_loader.py found in paper text by Syntology unverified MIT (permissive) · d18f0dd4a69ae09c · report
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