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Uncertainty Quantification(UQ)
Quantify and communicate AI confidence and uncertainty in predictions
Complexity: highInterpretability
In 30 seconds
- What
- Separates model uncertainty from irreducible noise, then reports prediction ranges and confidence levels alongside point estimates.
- When to use
- High-stakes decisions where users need to know prediction reliability, or when downstream systems must adjust behavior based on confidence.
- Watch out
- Poorly calibrated intervals mislead users into false confidence; validation on held-out data is essential and often skipped.
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Uncertainty Quantification: Overview
Quantify and communicate AI confidence and uncertainty in predictions
- Confidence interval estimation
- Epistemic vs aleatoric uncertainty
- Prediction interval visualization
- Calibrated probability outputs
- Uncertainty propagation tracking
- Risk-adjusted recommendations
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References
The papers, specifications, and repositories this pattern is based on.
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning - Gal & Ghahramani (ICML 2016)arXiv:1506.02142
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles - Lakshminarayanan et al. (NeurIPS 2017)arXiv:1612.01474
- On Calibration of Modern Neural Networks - Guo et al. (ICML 2017)arXiv:1706.04599