ML Uncertainty Quantification With Confidence Intervals and Model Switching
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Solution Overview
Problem
Machine learning models often produce unreliable results due to a lack of understanding of their underlying behavior, leading to errors and biases, and there is a need for comprehensive model governance to ensure ethical and responsible decision-making.
Innovation Solution
Implementing uncertainty quantification methods to estimate model uncertainty, generate confidence intervals, and switch to a more robust stepdown model when uncertainty exceeds a threshold, while reducing predictive variance by sparsifying neural networks to eliminate high-variance feature interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to perform complex predictive tasks, then productivity and functionality are improved, but reliability deteriorates due to lack of understanding of model behavior and potential biases
Solution Approach 1:
The patent implements a feedback mechanism where uncertainty values are calculated for each model output and fed back into the decision-making process. When uncertainty exceeds a threshold, the system automatically triggers alternative actions such as using a stepdown model or human review, creating a closed-loop system that continuously monitors and responds to model reliability
Solution Approach 2:
The patent introduces an intermediary uncertainty quantification layer between the machine learning model and the decision-making process. This intermediary calculates uncertainty values and confidence intervals, acting as a mediator that translates model outputs into reliability assessments, enabling informed decisions about whether to trust the model output
2Measurement precision
If complex machine learning models are deployed to handle intricate patterns, then measurement precision is improved, but ease of operation deteriorates due to difficulty in understanding and governing model behavior
Solution Approach 1:
The patent enables machine learning models to self-assess their own reliability by automatically calculating uncertainty values and confidence intervals for their outputs. This self-service mechanism eliminates the need for external manual assessment, allowing the model to autonomously determine when its predictions are reliable enough to be acted upon
Solution Approach 2:
The patent transforms the abstract concept of model reliability into concrete, measurable parameters such as uncertainty values and confidence intervals. By changing the representation of model output from simple predictions to predictions accompanied by quantitative reliability metrics, the system makes complex model behavior governable through parameter-based decision rules
3Reliability
If neural networks are sparsified to reduce predictive variance, then reliability is improved, but manufacturing precision deteriorates due to loss of model capacity
Solution Approach 1:
The patent implements a dynamic model selection strategy where the system transitions between different model configurations based on uncertainty thresholds. Instead of using a fixed model architecture, the system dynamically switches between the full-capacity model and a simplified stepdown model, optimizing the trade-off between performance and reliability for each specific prediction task
Data Source
AI summary
Computer-implemented machines, systems and methods for generating a confidence interval, in response to determining an uncertainty value associated with a first machine learning model output; switching from the first machine learning model to a second machine learning model, in response to determining the uncertainty value meets a threshold, wherein the second machine learning model generates a second machine learning model output; and providing to a user interface, the first machine learning output, the uncertainty value, the confidence interval, and the second machine learning output. The confidence interval may be represented as [max({tilde over (x)}−f(c)s, 0), min({tilde over (x)}+f(c)s, 1)], where c is first a desired confidence level, {tilde over (x)} represents sample scores sample mean, s represents the sample standard deviation, and f(c) represents an appropriate parametric multiplier.