iAdditive: fast and fair Shapley-inspired feature attribution for correlated features

Abstract

Interpreting machine learning models fairly and efficiently remains challenging, particularly when features are correlated. Classical Shapley-based explanations can split attribution among substitutes and are often computationally demanding. This study presents iAdditive, a model-agnostic approach that promotes fairness by grouping highly correlated or redundant features through group-level attribution semantics and improves efficiency via a dynamic coalition heuristic inspired by additive explanation methods. Experiments on simulated datasets with known structure and on NHANES indicate that iAdditive produces faithful global attributions under correlation while achieving substantial runtime reductions compared with KernelSHAP, TreeSHAP, SAGE, and exact Shapley baselines. By balancing fairness, interpretability, and efficiency, iAdditive provides a practical tool for trustworthy decision support in applications such as health care.