What products could be built from this research?

Why now—regulatory pressure (e.g., EU AI Act) demands explainable AI, and companies face rising costs from prediction errors in volatile markets. Existing tools like SHAP explain feature importance but don't quantify calibration vs. information loss, creating a gap for diagnostic products that align with maturing MLOps practices.

What are the practical use cases?

A bank uses the decomposition tool to audit its loan default prediction model, identifying that 60% of error comes from information loss in feature aggregation (e.g., oversimplified income categories), 30% from miscalibration, and 10% from irreducible uncertainty. This directs the team to refine feature engineering rather than blindly retraining the model, cutting false approvals by 15%.

Decomposing Probabilistic Scores: Reliability, Information Loss and Uncertainty

Decomposing Probabilistic Scores: Reliability, Information Loss and Uncertainty explores This paper explores the decomposition of probabilistic scores to analyze calibration and uncertainty in predictors.. Commercial viability score: 2/10 in Statistical Learning.

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Decomposing Probabilistic Scores: Reliability, Information Loss and Uncertainty