AI Model Snapshot Management for Poisoned Data Remediation
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Solution Overview
Problem
AI models can become untrustworthy if trained with poisoned data, leading to inaccurate inferences and inefficient use of computing resources for retraining.
Innovation Solution
The system manages AI models by obtaining snapshots during training, which allow for restoring a partially trained untainted model using a subset of the training dataset, and by screening new training data for anomalies to prevent poisoned data from being used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are retrained from scratch when poisoned data is detected, then model reliability is restored, but computational resource consumption increases significantly
Solution Approach 1:
The system takes preliminary actions by obtaining snapshots of the AI model at different training stages before poisoned data is introduced. When poisoning is detected, these pre-obtained snapshots enable restoration to a previous untainted state without requiring complete retraining, thus resolving the contradiction between maintaining reliability and reducing computational resource consumption.
Solution Approach 2:
The system discards the poisoned training data and recovers the AI model state from previously saved snapshots. This approach eliminates the need to retrain from scratch while removing the harmful influence of poisoned data, thereby restoring model reliability without incurring the full computational cost of complete retraining.
2Reliability
If complete retraining is performed to remove poisoned data influence, then inference accuracy is restored, but service interruption time increases
Solution Approach 1:
Snapshots are obtained in advance during normal training operations, preparing restoration points before poisoned data causes harm. When poisoning is detected, the system can quickly restore to a previous snapshot state, minimizing service interruption time while maintaining inference accuracy.
Solution Approach 2:
Instead of performing the time-consuming complete retraining process, the system skips directly to restoring from pre-saved snapshots. This rushing through the restoration process using prepared snapshots significantly reduces service interruption time while still achieving the goal of restoring accurate inferences.
3Measurement precision
If anomaly detection is performed on all training data, then poisoned data identification accuracy improves, but processing time increases
Solution Approach 1:
The training data processing is segmented into manageable portions, with snapshots obtained at different training stages. Anomaly detection is applied selectively to identify poisoned data segments without requiring exhaustive analysis of the entire training dataset, thus improving detection accuracy while controlling processing time.
Solution Approach 2:
The system performs partial anomaly detection on training data segments rather than exhaustive analysis of all data. By applying detection to specific portions and using snapshots to limit the scope of analysis, the system achieves sufficient poisoned data identification accuracy without the prohibitive processing time of complete data analysis.
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. 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.