A New Technique for AI Explainability using Feature Association Map
Current AI systems struggle to gain trust in high-stakes scenarios due to their lack of transparency, necessitating more accurate explanation methods. This work proposes FAMeX, an algorithm that introduces Feature Association Maps (FAMs) to model inter-feature dependencies using graph theory, thereby challenging the common assumption of feature independence in existing explainable AI (XAI) approaches. By capturing the contextual importance of features in classification tasks more faithfully, FAMeX provides a more realistic representation of feature contributions. Experimental results across eight benchmark datasets demonstrate that FAMeX significantly outperforms mainstream XAI methods such as Permutation Feature Importance (PFI) and SHAP in evaluating feature importance, exhibiting superior explanatory power and practical utility.