Machine Unlearning System for Removing Undesirable Inferences
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Solution Overview
Problem
Machine learning models lack transparency, leading to mistrust among end-users, especially domain experts, as they may produce undesirable inferences that contradict expert knowledge over time, and existing systems fail to effectively remove or correct these inferences.
Innovation Solution
A system that includes a search component to identify and select training data samples associated with rejected explanations based on similarity measures, and a machine unlearning unit to assign weightings or remove these samples, allowing for partial or complete removal of undesirable inferences, thereby enabling domain experts to improve model outputs and align them with expert knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained using historical data to improve predictive accuracy, then the model's ability to make accurate predictions improves, but undesirable inferences may be learned that contradict domain expert knowledge
Solution Approach 1:
The system performs preliminary identification of undesirable inferences in the training data before final model deployment. By using the explainer to identify training samples that produce explanations contradicting domain expert knowledge, and marking these samples for removal or reweighting before retraining, the system prevents unreliable inferences from being encoded into the final model while preserving accurate predictions from valid training data
Solution Approach 2:
The system implements a feedback loop where domain experts provide feedback on model explanations, the explainer identifies problematic training samples based on this feedback, and the model is retrained accordingly. This closed-loop feedback mechanism allows continuous improvement of model reliability by iteratively removing undesirable inferences while maintaining predictive accuracy through selective retention of valid training samples
2Reliability
If machine unlearning is applied to remove undesirable inferences, then model reliability improves, but computational resources and training time increase
Solution Approach 1:
The system extracts only the specific training samples that are associated with undesirable inferences identified by the explainer, rather than retraining on the entire dataset. By isolating and removing only the problematic subset of training data, the system achieves model unlearning with significantly reduced computational overhead and training time compared to complete retraining
Solution Approach 2:
The training data is segmented into desirable and undesirable portions based on explainer analysis. The system processes only the segmented undesirable portion for removal, allowing efficient selective unlearning. This segmentation approach divides the large training dataset into manageable subsets, enabling targeted intervention without the computational burden of handling the complete dataset
3Measurement precision
If complete retraining is performed to remove undesirable inferences, then model accuracy improves, but loss of useful learned patterns increases
Solution Approach 1:
The system extracts and removes only the specific training samples contributing to undesirable inferences, while preserving all other useful training samples. This selective extraction approach maintains the vast majority of useful learned patterns in the training dataset, preventing loss of valuable information while still achieving removal of problematic inferences through targeted sample exclusion
Solution Approach 2:
The system applies different quality assessments to different portions of the training data. Rather than treating all training samples uniformly, the explainer identifies locally which specific samples have undesirable quality (contradicting domain knowledge) versus those with good quality (aligning with expertise). This local quality differentiation allows preservation of useful patterns in high-quality samples while removing only the problematic low-quality samples
Data Source
AI summary
A method and system for removing undesirable inferences from a machine learning model include a search component configured to receive a rejected explanation of model output provided by the machine learning model, identify data samples to unlearn by selecting training samples from training data that were used to train the machine learning model, the selected training samples being associated with explanations that are similar to the rejected explanation according to a calculated similarity measure, and pass the data samples to unlearn to a machine unlearning unit.