Causal Training Data Validation for AI Poisoning Detection
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Solution Overview
Problem
AI models can become untrustworthy and computationally expensive to retrain when poisoned by malicious training data, leading to inefficient use of computing resources and unreliable inferences.
Innovation Solution
Implement a system that periodically snapshots AI model structures and causal relationships, allowing for efficient identification and remediation of poisoned training data by reverting to a last known good model and using incremental updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are retrained when poisoned training data is detected, then model reliability is restored, but computational resources and time are significantly consumed
Solution Approach 1:
The system performs preliminary identification of poisoned training data using causal relationship validation before the poisoning can fully compromise the model. By detecting causal violations in the training data beforehand, the system can prevent poisoned data from being used for model training, thereby avoiding the need for time-consuming retraining while maintaining model reliability
Solution Approach 2:
The system introduces an intermediary causal model that acts as a mediator between the training data and the AI model. This causal model validates the causal relationships in the training data before they are used to train the AI model, serving as a filtering mechanism that prevents poisoned data from reaching the model without requiring full retraining
2Reliability
If AI models are retrained when poisoned training data is detected, then model reliability is restored, but computing resources are significantly consumed
Solution Approach 1:
The system performs preliminary identification of poisoned training data using causal relationship validation before the poisoning can fully compromise the model. By detecting causal violations in the training data beforehand, the system can prevent poisoned data from being used for model training, thereby avoiding the need for computationally expensive retraining while maintaining model reliability
Solution Approach 2:
The system introduces an intermediary causal model that acts as a mediator between the training data and the AI model. This causal model validates the causal relationships in the training data before they are used to train the AI model, serving as a filtering mechanism that prevents poisoned data from reaching the model without requiring full retraining
3Reliability
If causal relationship analysis is performed on training data, then poisoned data can be identified proactively, but system complexity increases
Solution Approach 1:
The system segments the validation process into two distinct components: a causal model that learns causal relationships from clean data, and a validation mechanism that checks training data against these relationships. This segmentation allows the complex causal analysis to be performed in a modular fashion, where the causal model is trained separately on known good data and then used to validate subsequent training datasets
4Device complexity
If traditional training data validation is used, then system simplicity is maintained, but poisoned training data cannot be detected
Solution Approach 1:
The system introduces an intermediary causal model that acts as a mediator between the training data and the AI model. This causal model validates the causal relationships in the training data before they are used to train the AI model, serving as a filtering mechanism that prevents poisoned data from reaching the model without requiring full retraining
Data Source
AI summary
Methods and systems for identifying poisoned training data used for training artificial intelligence (AI) models are disclosed. To identify poisoned training data in a proposed training dataset, a causal model may be obtained. The causal model may include relationships relating data elements. The proposed training dataset may be identified as poisoned when data elements within the proposed training dataset do not satisfy the relationships set forth by the causal model. When the identification of poisoned training data is made, the AI model may not be updated using the proposed training dataset and the proposed training dataset may be discarded. If poisoned training data is not identified prior to training an AI model, methods and systems are disclosed for the remediation of the poisoned training dataset and subsequent tainted AI models. By doing so, the effect of poisoned training data may be prevented and/or efficiently computationally mitigated.