AI Model Release Training for Decision Order Consistency
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Solution Overview
Problem
Successive AI model releases often fail to preserve the decision value order, leading to unexpected false positives known as surprise false positives, due to variations in internal representations and decision rules between different model versions.
Innovation Solution
A processing device determines decision values and orders for data samples using a first AI model, trains a second AI model based on this order, and adjusts parameters using gradient and Hessian offsets to maintain consistency, employing techniques like gradient boosting and warm start training to preserve the decision value order.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If successive AI model releases are trained with updated data and improved algorithms, then model accuracy and adaptability are improved, but decision value order consistency deteriorates
Solution Approach 1:
The patent applies preliminary action by calculating decision value orders from the first AI model before training the second model, and using these pre-calculated orders as constraints during the training process. This ensures that the second model is guided to maintain consistent decision value ordering with the first model from the outset, rather than attempting to correct inconsistencies after training completes.
Solution Approach 2:
The patent changes parameters by introducing decision value order constraints as additional training parameters for the second AI model. These constraints modify the training objective function to penalize deviations from the first model's decision value order, thereby maintaining consistency while allowing the model to learn from updated data and improved algorithms.
2Adaptability or versatility
If AI model parameters are updated to improve performance, then model adaptability is improved, but classification stability deteriorates
Solution Approach 1:
The patent implements feedback by continuously monitoring the decision value orders produced by the second AI model during training and comparing them against the reference orders from the first model. This feedback loop allows the training process to adjust parameters in real-time to maintain classification stability while incorporating updates for improved adaptability.
Solution Approach 2:
The patent establishes the decision value order reference from the first model before beginning training of the second model. This preliminary establishment of expected ordering patterns provides a stable framework that guides the updated model's learning process, ensuring that adaptability improvements do not compromise classification stability.
3Productivity
If gradient boosting and warm start training are used to train successive models, then training efficiency is improved, but control over decision value order preservation becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the decision value orders from the first model before initiating the warm start training process. This preparation step simplifies the overall complexity by having the reference data ready, allowing the gradient boosting and warm start training to proceed efficiently with clear guidance on what ordering patterns to preserve.
Solution Approach 2:
The patent introduces decision value order constraints as an intermediary mechanism between the gradient boosting training process and the requirement for order preservation. This intermediary translates the abstract requirement of maintaining decision value order into concrete, actionable constraints that can be integrated into the efficient gradient boosting and warm start training framework.
Data Source
AI summary
The present disclosure provides an approach of producing, by a first artificial intelligence (AI) model, decision values corresponding to data samples in a validation dataset. The processing device determines a decision value order of the data samples based on the decision values. In turn, the processing device trains a second AI model based on the decision value order and the data samples to generate an output from an input dataset.