Global Gender Inequality Through Explainable AI: Machine Learning, Clustering, and SHAP Insights

Objective: This paper analyzes gender equality across countries in the year 2024 by using the GGGI, with the intention of disentangling the unseen structural and non-deterministic patterns. Instead of repeating the process of calculating the index, it is openly recognizing the compositional feature of the GGGI and the unseen similarities between the indices. Methods: This research employs a global cross-sectional study of 146 countries over the four primary GGGI sectors: economic participation, education, health and survival, and empowerment. Where OLS is only employed as a diagnostic test, as its almost perfect fit (R 2 ∼1) is squarely mechanical and lacks relevance for inference. Apart from ensemble models employed for predictions, K-means clustering, SHAP analysis, and GridSearchCV optimization are also used. Findings: The out-of-sample predictions demonstrate high levels of predictive accuracy, with Gradient Boosting models yielding an R 2 of approximately 0.90 and an RMSE of appr