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 approximately 0.045, indicating that there is significant nonlinear information beyond index aggregation. Unsupervised clustering techniques show that there are seven distinct country clusters that go beyond traditional geographic and income divisions, which can be identified with more than 93% accuracy. The SHAP results show that empowerment and economic participation are drivers, while there is insignificant variation in healthcare. Contribution: This study identifies the boundaries of regression analysis in index research, as well as the advantages of machine learning analysis in determining structural patterns related to gender equity.