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To interpret uncertainty estimates from differentiable probabilistic models,recent work has proposed generating a single Counterfactual Latent UncertaintyExplanation (CLUE) for a given data point where the model is uncertain,identifying a single, on-manifold change to the input such that the modelbecomes more certain in its prediction. We broaden the exploration to examine$\delta$-CLUE, the set of potential CLUEs within a $\delta$ ball of theoriginal input in latent space. We study the diversity of such sets and findthat many CLUEs are redundant; as such, we propose DIVerse CLUE($\nabla$-CLUE), a set of CLUEs which each propose a distinct explanation as tohow one can decrease the uncertainty associated with an input. We then furtherpropose GLobal AMortised CLUE (GLAM-CLUE), a distinct and novel method whichlearns amortised mappings on specific groups of uncertain inputs, taking themand efficiently transforming them in a single function call into inputs forwhich a model will be certain. Our experiments show that $\delta$-CLUE,$\nabla$-CLUE, and GLAM-CLUE all address shortcomings of CLUE and providebeneficial explanations of uncertainty estimates to practitioners.

Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates