  This paper applies eXplainable Artificial Intelligence (XAI) methods to
investigate the socioeconomic disparities in COVID patient mortality. An
Extreme Gradient Boosting (XGBoost) prediction model is built based on a
de-identified Austin area hospital dataset to predict the mortality of COVID-19
patients. We apply two XAI methods, Shapley Additive exPlanations (SHAP) and
Locally Interpretable Model Agnostic Explanations (LIME), to compare the global
and local interpretation of feature importance. This paper demonstrates the
advantages of using XAI which shows the feature importance and decisive
capability. Furthermore, we use the XAI methods to cross-validate their
interpretations for individual patients. The XAI models reveal that Medicare
financial class, older age, and gender have high impact on the mortality
prediction. We find that LIME local interpretation does not show significant
differences in feature importance comparing to SHAP, which suggests pattern
confirmation. This paper demonstrates the importance of XAI methods in
cross-validation of feature attributions.
