Feature Group Influence Analysis for AI Model Interpretability

The patent introduces Shapley value analysis as an intermediary mechanism that bridges the gap between complex machine learning models and human interpreters. By computing feature importance scores through Shapley values, the system provides a mediator layer that translates model decisions into comprehensible explanations without altering the underlying model architecture or decision-making process.

The patent replaces traditional mechanical explanation methods (such as rule-based systems or feature importance plots) with a game-theoretic approach using Shapley values. This substitution provides a mathematically rigorous framework for attribute importance analysis that better handles complex interactions between features while maintaining interpretability.

The patent segments the feature set into multiple groups or subsets, allowing Shapley value computations to be performed on smaller groups rather than all features simultaneously. This segmentation reduces the computational burden of calculating Shapley values while still providing comprehensive interpretability across the entire feature set through aggregated results.

The patent computes Shapley values for a selected subset of features or feature groups rather than exhaustively computing them for all possible feature combinations. This partial action approach provides sufficient interpretability for practical purposes while significantly reducing computational time and resources required.