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Diverse and Amortised Counterfactual Explanations for Uncertainty Estimates
Dan Ley · Umang Bhatt · Adrian Weller

To interpret uncertainty estimates from differentiable probabilistic models, Antorán et al. (2021) proposed generating a single Counterfactual Latent Uncertainty Explanation (CLUE) for a given data point where the model is uncertain. Ley et al. (2021) formulated δ-CLUE, the set of CLUEs within a δ ball of the original input in latent space- however, we find that many CLUEs generated by this method are very similar, hence redundant. Here we propose DIVerse CLUEs (∇-CLUEs), a set of CLUEs which each provide a distinct explanation as to how one can decrease the uncertainty associated with an input. We further introduce GLobal AMortised CLUEs (GLAM-CLUEs), which represent amortised mappings that apply to specific groups of uncertain inputs, taking them and efficiently transforming them in a single function call into inputs that a model will be certain about. Our experiments show that ∇-CLUEs and GLAM-CLUEs both address shortcomings of CLUE and provide beneficial explanations of uncertainty estimates to practitioners.

Author Information

Dan Ley (University of Cambridge)
Umang Bhatt (University of Cambridge)
Adrian Weller (University of Cambridge, Alan Turing Institute)
Adrian Weller

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, and is a Turing AI Fellow leading work on trustworthy Machine Learning (ML). He is a Principal Research Fellow in ML at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. Previously, Adrian held senior roles in finance. He received a PhD in computer science from Columbia University, and an undergraduate degree in mathematics from Trinity College, Cambridge.

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