  With the universal adoption of machine learning in healthcare, the potential
for the automation of societal biases to further exacerbate health disparities
poses a significant risk. We explore algorithmic fairness from the perspective
of feature selection. Traditional feature selection methods identify features
for better decision making by removing resource-intensive, correlated, or
non-relevant features but overlook how these factors may differ across
subgroups. To counter these issues, we evaluate a fair feature selection method
that considers equal importance to all demographic groups. We jointly
considered a fairness metric and an error metric within the feature selection
process to ensure a balance between minimizing both bias and global
classification error. We tested our approach on three publicly available
healthcare datasets. On all three datasets, we observed improvements in
fairness metrics coupled with a minimal degradation of balanced accuracy. Our
approach addresses both distributive and procedural fairness within the fair
machine learning context.
