Heuristic XAI: AIME and HMM Integration Improved-Feature-Importance Assessment Through Heuristics

The explainability of AI and machine learning models has become crucial, drawing significant attention to explainable AI (XAI) technologies. Global feature importance extraction is key to understanding the behavior of these models. However, existing XAI techniques often overlook the effects of multicollinearity in data, limiting their accuracy. We propose a method that combines Approximate Inverse Model Explanations (AIME) with Hidden Markov Models (HMMs). The HMMs are used to extract hidden states, and the local feature importance for each state is calculated. These values are then used to generate heuristics that adjust the importance of global features. We evaluated our method using the Wine Quality, Census Income, Gas Sensor Array Drift at Different Concentrations, and Breast Cancer Wisconsin datasets. The results showed that our method effectively handles strong multicollinearity and provides approximately 20% more accurate feature importance assessments than previous methods depending on the dataset. Therefore, our approach can better represent XAI contributions using algorithmic or human-supplied heuristics. Our study confirms that this approach is valid for XAI and enables the integration of algorithmic or human-supplied heuristics to realize XAI, which can significantly benefit various fields by enhancing model interpretability and decision-making processes.