Counterfactual Distance Analysis for AI Model Retraining

The patent introduces counterfactual samples as an intermediary between the target samples and the model performance evaluation. Instead of directly comparing target samples to training data distribution (which causes false positives), the system generates counterfactual samples that represent boundary cases and uses the distance between target samples and their counterfactuals as a mediator to infer model performance. This indirect measurement approach reduces false positives while maintaining reliability.

The patent replaces the conventional statistical distance measurement approach (mechanical system) with an AI-based counterfactual generation and distance computation approach. Instead of using traditional statistical methods to measure distribution distance, the system uses neural networks to generate counterfactual samples and compute distances in the feature space, providing more accurate and reliable performance analysis with fewer false positives.

The patent creates a universal performance analysis framework that works with any AI model type regardless of whether it provides confidence scores. The counterfactual-based approach serves multiple functions: it can evaluate classification models, regression models, and any other model type without requiring model-specific calibration or confidence score outputs. This makes the system universally applicable while still providing deep insights into model behavior.

The patent extracts the essential performance evaluation capability from confidence score-based methods without requiring the confidence scores themselves. By separating the performance analysis function from the model's output characteristics (confidence scores), the system can obtain model behavior insights from any model type, extracting only the necessary information (predictions and features) needed for counterfactual generation and distance computation.