Inference Model Reversion via Cost-Benefit Analysis
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Solution Overview
Problem
Inference models can be compromised by poisoned training data during updates, leading to undesirable inferences, and existing methods for reverting these models are resource-intensive, posing challenges in managing computing resources effectively.
Innovation Solution
A system and method for managing inference models that includes identifying compromised models, estimating resource costs for reverting them to uncompromised or partially compromised states, and using a graphical user interface to present users with options for reversions, allowing them to select the most resource-efficient plan based on cost and benefit analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inference models are updated with new training data, then model performance and accuracy are improved, but the models may be compromised by poisoned training data
Solution Approach 1:
The system performs preliminary actions by maintaining historical versions of inference models before updates occur. When poisoned training data is detected, the system can revert to a previous clean version of the model, preventing the harmful effects of poisoned data from permanently degrading model performance.
Solution Approach 2:
The system implements feedback mechanisms to detect and identify poisoned training data before it compromises the model. By monitoring model behavior and training data characteristics, the system can provide feedback about potential poisoning attempts and trigger reversion processes to restore model integrity.
2Reliability
If reversion operations are performed to remove poisoned data impact, then model integrity is restored, but significant computing resources are consumed
Solution Approach 1:
Instead of always performing complete reversion operations that consume maximum resources, the system applies partial reversion by selectively reverting only to the most recent clean version of the model before poisoning occurred. This partial action approach restores model integrity while minimizing computing resource consumption compared to complete reversion.
Solution Approach 2:
The system changes the parameter of reversion granularity by allowing users to select different reversion targets (e.g., revert to specific historical versions). This enables optimization of computing resource usage by reverting only as much as necessary to remove poisoned data impact, rather than always performing full reversion.
3Measurement precision
If users manually select reversion options, then reversion precision is improved, but cognitive burden on users increases
Solution Approach 1:
The system performs self-service by automatically identifying poisoned training data and proposing appropriate reversion actions without requiring user analysis. The system autonomously evaluates the situation, determines the best reversion target, and executes the reversion process, thereby maintaining high precision while eliminating the cognitive burden on users.
Solution Approach 2:
The system provides feedback to users in the form of automated recommendations and explanations for proposed reversion actions. This feedback mechanism helps users understand why a particular reversion is suggested, allowing them to make informed decisions without bearing the full cognitive burden of analyzing the situation themselves.
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 to be performed may be selected based on the cost for performing the reversion and benefits provided by the reverted inference model.