Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. (arXiv:2208.06648v2 [cs.AI] UPDATED)

Biases have marked medical history, leading to unequal care affecting
marginalised groups. The patterns of missingness in observational data often
reflect these group discrepancies, but the algorithmic fairness implications of
group-specific missingness are not well understood. Despite its potential
impact, imputation is too often an overlooked preprocessing step. When
explicitly considered, attention is placed on overall performance, ignoring how
this preprocessing can reinforce group-specific inequities. Our work questions
this choice by studying how imputation affects downstream algorithmic fairness.
First, we …

algorithmic fairness arxiv fairness impact imputation strategies