Automated Risk Analysis for Machine Learning Models
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Solution Overview
Problem
Current methods for risk analysis of machine learning models are expensive, time-consuming, and impractical, failing to effectively assess the uncertainty and risk of predictive models before deployment, especially when future cases differ from training data.
Innovation Solution
A system and method that generates synthetic datasets with similar statistical properties to the training data, allowing for quick and user-friendly risk assessment by analyzing differences in prediction distributions and outcomes, enabling the estimation of uncertainty or risk before production use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional risk analysis methods are used to assess predictive models, then measurement precision of risk is improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The patent creates synthetic copies of the training dataset that preserve the statistical properties and data generation process characteristics. These synthetic datasets serve as proxies for future production data, allowing risk assessment without requiring actual future data or extensive computational experiments. The copying principle enables rapid evaluation by working with generated replicas rather than real data.
Solution Approach 2:
The patent performs risk assessment actions before the model is deployed to production. By evaluating the model's performance on synthetic data generated from the training process beforehand, the system identifies potential issues and miscalibrations prior to deployment, preventing costly retraining and reducing the time needed for post-deployment monitoring and adjustment.
2Reliability
If comprehensive risk analysis is performed on production data, then reliability of risk assessment is improved, but device complexity and ease of operation worsen
Solution Approach 1:
Instead of analyzing actual production data which would require complex integration with production systems, the patent uses synthetic copies that replicate the essential statistical properties. This approach maintains reliability by preserving the data generation process characteristics while avoiding the complexity of accessing and processing real production data streams.
Solution Approach 2:
The system uses the model's own training data and training process characteristics to generate synthetic datasets for self-evaluation. The model essentially assesses itself by comparing its performance on synthetic data (representing future cases) versus its known performance on training data, eliminating the need for external production data or complex external validation systems.
3Manufacturing precision
If traditional model training and validation is performed, then manufacturing precision of the model is improved, but loss of information about future performance uncertainty worsens
Solution Approach 1:
The patent creates synthetic datasets that copy the statistical properties and variability of the training data, then uses these copies to evaluate model performance under different conditions. This process reveals information about performance uncertainty and potential miscalibrations that traditional validation on fixed training/test splits cannot detect, as the synthetic data can be regenerated with different random seeds to show performance variation.
Solution Approach 2:
The patent changes the parameters of the synthetic data generation process (such as random seeds, sampling methods, or data transformation parameters) to create multiple variations of the training data. By evaluating model performance across these parameter variations, the system captures information about performance sensitivity and uncertainty that would be lost in traditional fixed-parameter validation approaches.
Data Source
AI summary
There is risk or uncertainty that a predictive model, such as a machine learning model, that has been fitted to a dataset of known cases will not produce the same distribution of predictions or outcomes when applied to future cases. To assess risk, the dataset of known cases is statistically assessed using best-fit probability distributions and correlations for one or more features of the dataset, then new cases are generated to produce a synthetic dataset that has statistical characteristics similar to the known dataset. The predictive model can be applied to the synthetic dataset. A comparison of the distribution of predictions by the predictive model on the known dataset and on the synthetic dataset can be made. Significant variations in the distribution of predictions or outcomes can indicate that the model is not suitable for future cases. Lack of such variations can increase confidence and willingness to use the model.