Risk-Aware Model Wrappers for Uncertainty and Bias Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural networks (NNs) face unexpected failures due to systemic issues like data imbalances and noise, leading to algorithmic bias and predictive uncertainty, and current risk-awareness algorithms are complex, ad-hoc, and require significant manual modifications, limiting their scalability and robustness.
Innovation Solution
A unified framework for quantifying risk in machine learning models, allowing automatic estimation and composition of different risk metrics, such as aleatoric, epistemic, and bias estimation, using model-agnostic wrappers that modify computational graphs to provide comprehensive risk awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing risk-awareness algorithms are used, then risk estimation can be performed, but the process requires significant manual modifications and takes three or more months, reducing productivity
Solution Approach 1:
The framework segments risk estimation into separate, modular risk modules (aleatoric uncertainty, epistemic uncertainty, bias estimation) that can be independently applied to model components. This allows automated transformation of models into risk-aware variants without manual intervention, reducing the time required from months to seconds while maintaining comprehensive risk assessment capability
Solution Approach 2:
The system automatically changes model parameters and architecture by applying risk modules as transformations to the computational graph. These parameter changes enable risk-awareness without manual modification, allowing the framework to automatically adjust model components based on risk metrics while preserving the original model's functionality
2Reliability
If existing risk algorithms are applied, then risk assessment can be performed, but significant engineering changes are required, increasing device complexity
Solution Approach 1:
The framework provides a universal risk estimation platform that can apply different risk modules to various model architectures and settings without requiring custom engineering changes. The modular design allows the same framework to handle aleatoric uncertainty, epistemic uncertainty, and bias estimation across different domains, reducing complexity while maintaining comprehensive risk awareness
Solution Approach 2:
The framework introduces intermediary risk modules that act as mediators between the original model and risk assessment. These modules transform the computational graph by inserting risk estimation layers without requiring changes to the core model architecture, thereby reducing engineering complexity while achieving comprehensive risk awareness
3Measurement precision
If specialized risk estimation algorithms are used, then specific risk metrics can be estimated, but composability is limited and scope is narrow, reducing adaptability
Solution Approach 1:
The framework merges multiple risk estimation algorithms into a unified, composable structure where aleatoric uncertainty, epistemic uncertainty, and bias estimation modules can be combined in various configurations. This allows precise estimation of specific risk metrics while maintaining high adaptability through composability, enabling users to select and combine modules based on their specific needs across different domains
Data Source
AI summary
Disclosed techniques include receiving a user model as a computational graph and a list of risk metrics indicating types of risk factors to assess, and selecting at least one composable wrapper for wrapping the user model based on a risk metric(s) in the list. The techniques further include determining a set of graph-level transformations to apply to the computational graph representation that implement a risk estimate for the user model, applying the set of graph-level transformations to modify the operations performed by the user model, and generating, based on the transformed computational graph, a modified executable variant of the user model that, when executed within a machine learning framework, produces: outputs that preserve the structure and interpretation of the user model outputs; and corresponding uncertainty estimates. Still further, the techniques include executing the modified executable variant risk-aware variant of the user model to produce prediction output(s) and/or corresponding uncertainty estimate(s).