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About this paper
Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato
Researchers from the University of Cambridge and The Alan Turing Institute developed Counterfactual Latent Uncertainty Explanations (CLUE), a method to interpret predictive uncertainty in differentiable probabilistic models like Bayesian Neural Networks. CLUE identifies minimal, in-distribution input changes that would reduce a model's uncertainty, providing actionable insights for practitioners and enhancing trustworthy AI.
Key insights
- Explaining the *sources* of model uncertainty, rather than just its predictions, is a critical missing component for developing truly trustworthy and actionable AI systems.
- Leveraging the constrained latent space of a deep generative model enables the generation of plausible and in-distribution counterfactual explanations, overcoming limitations of direct input-space modifications.
- A computationally grounded evaluation framework, utilizing a 'ground truth' data generating process, offers a robust and principled way to objectively assess the informativeness and relevance of uncertainty explanations.
Summary by alphaXiv.
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