Behavior Modeling Architecture for ML Safety Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models, particularly AI systems, face challenges in ensuring safety due to blind spots from adversarial perturbations and lack of interpretability, which can lead to incorrect predictions and safety concerns in autonomous systems.
Innovation Solution
A behavior modeling architecture that integrates conditions, events, and triggers to monitor probability likelihoods and trigger system-knowledge injection in white-box models, enabling real-time monitoring and formal verification to maintain system behavior within defined boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used for autonomous decision-making, then productivity and automation are improved, but reliability and safety deteriorate due to blind spots from adversarial perturbations and lack of interpretability
Solution Approach 1:
The patent introduces a behavioral model as an intermediary layer between the ML model and the autonomous system. This behavioral model serves as a mediator that verifies whether ML model outputs conform to expected safety constraints and behavioral specifications, thereby resolving the safety reliability issue without reducing productivity
Solution Approach 2:
The patent implements a feedback mechanism where the behavioral model continuously monitors ML model predictions and provides corrective feedback when predictions violate safety constraints. This feedback loop enables the system to maintain high automation while ensuring reliability through continuous verification and correction
2Productivity
If complex ML models are deployed for accurate predictions, then productivity is improved, but ease of operation deteriorates due to lack of interpretability
Solution Approach 1:
The behavioral model acts as an intermediary that translates complex ML model predictions into interpretable behavioral specifications. By verifying predictions against human-understandable behavioral rules, the system maintains high prediction accuracy while improving interpretability for operators
Solution Approach 2:
The patent creates a simplified copy or representation of the ML model's behavior through the behavioral model. This behavioral specification serves as an interpretable counterpart to the complex ML model, allowing operators to understand and verify system behavior without needing to interpret the complex internal workings of the ML model
3Reliability
If formal verification methods are applied to ML models, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the verification task into two parts: (1) using established formal verification methods to verify the behavioral model against safety specifications, and (2) using the verified behavioral model to verify ML model predictions. This segmentation reduces the overall complexity by breaking down the verification problem into manageable components
Data Source
AI summary
An exemplary embodiment may present a behavior modeling architecture that is intended to assist in handling, modelling, predicting and verifying the behavior of machine learning models to assure the safety of such systems meets the required specifications and adapt such architecture according to the execution sequences of the behavioral model. An embodiment may enable conditions in a behavioral model to be integrated in the execution sequence of behavioral modeling in order to monitor the probability likelihoods of certain paths in a system. An embodiment allows for real-time monitoring during training and prediction of machine learning models. Conditions may also be utilized to trigger system-knowledge injection in a white-box model in order to maintain the behavior of a system within defined boundaries. An embodiment further enables additional formal verification constraints to be set on the output or internal parts of white-box models.