δ-CLUE: Diverse Sets of Explanations for Uncertainty Estimates

Multiple explanations reveal model behavior more completely than single explanations: δ-CLUE generates diverse sets of counterfactual explanations rather than just one, allowing stakeholders to see multiple ways a model could become more confident about its predictions for the same input. This plurality helps identify whether model uncertainty stems from specific input features or broader patterns.

Explanations stay grounded in realistic data: The approach constrains explanations to remain "on the data manifold" and within a δ ball in latent space, meaning the counterfactual examples generated are plausible variations rather than unrealistic or adversarial modifications. This makes explanations more trustworthy for understanding real-world model behavior.

Addresses a key limitation of prior uncertainty interpretation methods: The original CLUE approach could produce many different explanations for a single input due to lack of constraints. δ-CLUE solves this by systematically generating diverse but bounded sets of explanations, providing more structured and interpretable uncertainty estimates for probabilistic models.

Relevant for high-stakes AI deployment: By clarifying what would make a model more confident in its predictions, this approach helps practitioners and auditors understand whether uncertainty estimates are reliable—critical information for deploying AI systems in domains like healthcare, finance, or criminal justice where confidence calibration matters.