ConformaDecompose: Explaining Uncertainty via Calibration Localization

Conformal prediction provides guaranteed accuracy but lacks transparency: While conformal prediction offers mathematically guaranteed coverage for prediction intervals, it uses a single global threshold that doesn't explain why predictions are uncertain for specific cases—making it difficult to understand whether uncertainty is unavoidable or could be reduced.

The framework distinguishes between different types of uncertainty: ConformaDecompose helps identify whether uncertainty comes from irreducible noise in the data itself (aleatoric) versus uncertainty from model limitations or data mismatches (epistemic), which is crucial for determining if predictions could be improved.

Instance-level explanations reveal hidden patterns: By analyzing uncertainty on a case-by-case basis rather than globally, the approach uncovers different uncertainty regimes across tasks that would be invisible when only looking at overall interval widths—enabling more targeted improvements to AI systems.

Reducible uncertainty can be diagnosed without changing the model: The diagnostic approach allows practitioners to understand which parts of prediction uncertainty could theoretically be reduced through better calibration or training data, without requiring modifications to the underlying predictor or sacrificing the coverage guarantees that make conformal prediction valuable.

Practical value for responsible AI deployment: For organizations deploying AI systems, this framework provides actionable insights into prediction reliability at the individual prediction level, helping teams decide when to trust model outputs and where additional data collection or model refinement might be most beneficial.