Instance level analysis of equitable learning via dissimilar variable grouping on healthcare datasets.

Machine learning models are increasingly deployed in healthcare, but their complexity can propagate biases and obscure decision-making that disproportionately affect vulnerable populations. While existing explainability methods focus on variable-level attribution, they often fail to capture how predictions and explanations behave at the instance level. To address this gap, we introduce SHIELD: a SHapley and Information-theory based framework for Equitable Learning via Dissimilar variable grouping. In this framework, variables are grouped using conditional mutual information to weaken correlations that may encode sensitive attributes. Group-specific autoencoders learn latent representations that preserve a mapping to the original variables, allowing SHapley Additive exPlanations (SHAP) to be computed in the original variable space for faithful instance-level attribution. Experiments on three clinical datasets demonstrated that dissimilarity-based grouping produced a more even distribution of variable importance and increased total attribution magnitude, suggesting broader usage of variables in model decision-making. On average, grouping improved the six evaluated fairness measures, covering both outcome and explanation disparities by 17.09%. Nevertheless, predictive performance decreased by 6.18% across six metrics. These changes represent a context-dependent trade-off rather than directly comparable gains and losses. Overall, SHIELD provides a principled and reproducible framework for equitable and explainable machine learning in health informatics, with code available to support future research.