Uncertainty Quantification Analysis
Service Description
This module assesses whether the uncertainty estimates provided by an AI model are well-calibrated, meaningful, and suitable for supporting safe decision-making in clinical or high-stakes contexts.
The customer-provided model's uncertainty outputs (e.g., confidence scores, predictive distributions, epistemic/aleatoric uncertainty estimates) are evaluated for calibration, sharpness, and reliability. Evaluation can be performed with or without ground truth labels, depending on availability: • With ground truths: Direct assessment of uncertainty quality can be conducted by evaluating how well uncertainty scores align with truly uncertain outcomes. This includes analysis on real out-of-distribution (OOD) samples and ambiguous cases (e.g., annotator disagreement), enabling a precise evaluation of whether high uncertainty corresponds to genuinely difficult or uncertain predictions. • Without ground truths: Proxy tasks can be employed to assess uncertainty quantification capabilities, including synthetic out-of-distribution sample generation, Expected Calibration Error (ECE), Prediction Rejection Ratio (PRR) analysis, and correctness prediction assessment. While less direct, these approaches still provide meaningful insights into model uncertainty quality.
Evaluation Outcomes:
Positive Indicators • Well-calibrated confidence scores (predicted probabilities reflect true outcome frequencies) • Appropriate uncertainty increases for ambiguous, atypical, or out-of-distribution inputs • Meaningful separation between epistemic and aleatoric uncertainty (if applicable)
Negative Indicators • Overconfident predictions on uncertain or out-of-distribution cases • Poorly calibrated probability estimates • Uncertainty scores that do not correlate with prediction errors
Provider & Contact
Pricing is defined on a case‑by‑case basis and depends on the specific customer requirements, model characteristics, and validation scope. A detailed offer can be prepared upon request.