Inference Model Reversion via Poisoned Data Detection
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Solution Overview
Problem
Inference models used for computer-implemented services can become tainted by poisoned training data during updates, leading to undesirable inferences, and reverting these models to remove the influence of poisoned data incurs significant resource costs.
Innovation Solution
A system and method for managing inference models through reversion, where users are presented with graphical user interfaces to explore options for performing different types of reversions, estimate resource costs, and select portions of training data for use in reversions, thereby balancing resource consumption with benefits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If inference models are updated with new training data, then model functionality and service capabilities are improved, but the risk of model tainting by poisoned training data increases
Solution Approach 1:
The system performs preliminary actions by maintaining version history and checkpoints of training data and model states before updates. When poisoned training data is detected, the system can revert to previous clean versions, preventing the propagation of tainted data while preserving the ability to update with new functionality.
Solution Approach 2:
The system introduces an intermediary layer (version control system) between training data updates and model deployment. This intermediary tracks and manages different versions of training data and models, enabling safe updates by comparing new data against established baselines and allowing controlled reversion when contamination is detected.
2Reliability
If reversions are performed to remove influence of poisoned training data, then model integrity is restored, but resource costs increase significantly
Solution Approach 1:
The system extracts and removes only the specific poisoned training data or model components that are contaminated, rather than performing complete model reversions. This targeted extraction approach restores model integrity by eliminating the harmful influence while preserving the majority of the model and its training, thereby reducing resource costs.
Solution Approach 2:
The system changes parameters by adjusting the extent and scope of reversion operations based on the detected contamination level. Instead of always performing full reversions, the system dynamically determines the appropriate reversion scope, reducing computational resources while maintaining sufficient model integrity.
3Reliability
If complete reversion is performed to remove all poisoned training data influence, then model integrity is maximized, but training time and computational resources are significantly consumed
Solution Approach 1:
The system applies partial action by performing reversion only to the extent necessary to remove poisoned data influence. Rather than always executing complete model reversions, the system determines the minimal required reversion scope based on contamination detection, thereby reducing training time while maintaining adequate model integrity.
4Ease of operation
If users are provided with detailed options for reversion, then user control and decision-making are improved, but cognitive burden on users increases
Solution Approach 1:
The system enables self-service by automatically detecting poisoned training data and presenting pre-evaluated reversion options to users. The system performs the complex analysis of training data contamination and prepares reversion recommendations, allowing users to make informed decisions without bearing the full cognitive burden of analyzing raw technical data.
Data Source
AI summary
Methods and systems for managing inference models are disclosed. The inference models may be used to provide computer implemented services by generating inferences used in the services. The inference models may be managed by reverting inference models that are found to be compromised through training with poisoned training data. The type of reversion and training data to be used may be selected based on the cost for performing the reversion and benefits provided by the reverted inference model using graphical user interfaces.