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This paper applies eXplainable Artificial Intelligence (XAI) methods toinvestigate the socioeconomic disparities in COVID patient mortality. AnExtreme Gradient Boosting (XGBoost) prediction model is built based on ade-identified Austin area hospital dataset to predict the mortality of COVID-19patients. We apply two XAI methods, Shapley Additive exPlanations (SHAP) andLocally Interpretable Model Agnostic Explanations (LIME), to compare the globaland local interpretation of feature importance. This paper demonstrates theadvantages of using XAI which shows the feature importance and decisivecapability. Furthermore, we use the XAI methods to cross-validate theirinterpretations for individual patients. The XAI models reveal that Medicarefinancial class, older age, and gender have high impact on the mortalityprediction. We find that LIME local interpretation does not show significantdifferences in feature importance comparing to SHAP, which suggests patternconfirmation. This paper demonstrates the importance of XAI methods incross-validation of feature attributions.

Using Explainable AI to Cross-Validate Socio-economic Disparities Among Covid-19 Patient Mortality