AI Model Management via Directed Modification Detection
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Solution Overview
Problem
AI models can become untrustworthy due to poisoned training data, leading to inaccurate inferences and requiring computationally expensive retraining, which disrupts inference generation and consumes valuable computing resources.
Innovation Solution
Implementing AI model snapshots to restore a partially trained untainted model using a subset of the training dataset and performing directed modification analysis to screen new training data for potential poisoning, reducing the likelihood of introducing poisoned data and conserving computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are retrained to remove poisoned training data influence, then model reliability is improved, but computing resources are consumed and inference generation is interrupted
Solution Approach 1:
The system performs preliminary detection of poisoned training data before it corrupts the AI model. By screening training data using directed modification analysis that compares new training data against the existing training dataset, the system identifies poisoned data in advance and prevents model retraining, thereby maintaining both reliability and continuous inference generation
Solution Approach 2:
The patent introduces an intermediary detection mechanism between the training data and the AI model. This intermediary system performs directed modification analysis to identify poisoned data, acting as a filter that prevents corrupted data from reaching the model without requiring model retraining or service interruption
2Reliability
If AI models are retrained to remove poisoned training data influence, then model reliability is improved, but computing resources are consumed
Solution Approach 1:
The system performs preliminary detection of poisoned training data before it corrupts the AI model. By screening training data using directed modification analysis that compares new training data against the existing training dataset, the system identifies poisoned data in advance and prevents model retraining, thereby maintaining both reliability and continuous inference generation
Solution Approach 2:
The system uses the existing training dataset to screen new training data through directed modification analysis. This self-service approach leverages the model's own training data as a reference for detecting poisoned data, eliminating the need for external detection resources and avoiding computationally expensive model retraining
3Reliability
If directed modification analysis is performed to screen new training data, then the likelihood of introducing poisoned data is reduced, but additional computing resources are required for screening
Solution Approach 1:
The system uses the existing training dataset to screen new training data through directed modification analysis. This self-service approach leverages the model's own training data as a reference for detecting poisoned data, eliminating the need for external detection resources and avoiding computationally expensive model retraining
Solution Approach 2:
The system creates a reference copy of the existing training dataset to use in directed modification analysis. By comparing new training data against this reference copy, the system can identify poisoned data through similarity analysis without requiring additional complex detection infrastructure
Data Source
AI summary
Methods and systems for managing artificial intelligence (AI) models are disclosed. As AI models are updated over time using new training data, the new training data may be screened and snapshots of the AI models may be obtained. The new training data may be screened by ascertaining whether it is likely that the new training data is synthetic through directed modification analysis. If likely synthetic, the new training data may be treated as being poisoned and screen. Screening the new training data may reduce the likelihood that AI models become tainted and provide undesired inferences. The snapshots may be used to remediate tainted AI models when trained using poisoned training data that was not screened. Use of snapshots may reduce the computational expense for remediating the impact of poisoned training data.