AI Model Certification System for Invertibility and Interpretability
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Solution Overview
Problem
Current AI models based on Deep Learning are prone to overfitting, lack interpretability, and suffer from catastrophic forgetting, making them difficult to modify and adapt to new tasks, and are vulnerable to exploiting biases in training data.
Innovation Solution
A certification system that evaluates AI models against Robustness, Interpretability, Security, and Efficiency (RISE) metrics, ensuring they are invertible, adaptable, and secure, thereby improving their transparency and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Deep Learning models are used to achieve high prediction accuracy, then the model performance is improved, but the interpretability and understandability of the model mechanism deteriorates
Solution Approach 1:
The patent introduces an intermediary verification system that acts as a mediator between the AI model and users. This system includes verification modules that analyze model behavior, generate verification reports, and provide certificates of correctness without requiring users to understand the complex internal mechanisms of the DL model itself.
Solution Approach 2:
The patent replaces the need for human understanding of complex mathematical transformations with an automated verification system. Instead of requiring users to comprehend the mechanical operations of neural networks, the system uses formal verification methods to mathematically prove model correctness and generate interpretable verification results.
2Adaptability or versatility
If the AI model is retrained to solve a new task, then the new task performance is improved, but the ability to solve the original task deteriorates due to catastrophic forgetting
Solution Approach 1:
The patent implements feedback mechanisms where the verification system continuously monitors model performance across multiple tasks. The verification reports provide feedback on whether the model maintains correctness on original tasks while learning new tasks, enabling detection and mitigation of catastrophic forgetting through iterative verification and model updates.
Solution Approach 2:
The patent performs preliminary verification of model correctness before retraining for new tasks. By establishing a baseline verification status and using this as a starting point, the system can detect when catastrophic forgetting occurs and take corrective actions such as fine-tuning or retraining on combined datasets.
3Measurement precision
If the AI model exploits biases in training data to achieve high accuracy on training examples, then the training performance is improved, but the generalization ability to new data deteriorates
Solution Approach 1:
The patent applies preliminary anti-action by using the verification system to detect biased decision-making patterns before they cause generalization failures. The verification modules analyze whether the model relies on spurious correlations or biased features, and generate warnings or corrections to prevent poor generalization performance.
4Measurement precision
If the AI model complexity is increased to improve performance, then the prediction accuracy is improved, but the ease of operation and maintenance deteriorates
Solution Approach 1:
The patent introduces an intermediary verification system that simplifies the operation and maintenance of complex AI models. The verification modules automatically analyze model correctness, generate detailed verification reports, and provide certificates that make it easier for users to operate and maintain complex models without needing to understand their internal complexity.
Data Source
AI summary
The disclosure includes embodiments of a method for a certification system for an artificial intelligence (AI) model. According to some embodiments, the method includes analyzing the AI model to determine that the AI model is compliant with the set of metrics. The method includes certifying the AI model responsive to determining that the AI model is compliant with the set of metrics. The set of metrics includes verifying that at least one layer Z of the AI model is invertible. The method includes certifying the AI model responsive to determining that the AI model is compliant with the set of metrics. In some embodiments, if the AI model includes a plurality of layers Z and the set of metrics verify that each of the layers Z is invertible, then AI model is certified as an “invertible AI model.”