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 provide a structured view of the relationship between clinical
presence mechanisms and group-specific missingness patterns. Then, through
simulations and real-world experiments, we demonstrate that the imputation
choice influences marginalised group performance and that no imputation
strategy consistently reduces disparities. Importantly, our results show that
current practices may endanger health equity as similarly performing imputation
strategies at the population level can affect marginalised groups differently.
Finally, we propose recommendations for mitigating inequities that may stem
from a neglected step of the machine learning pipeline.

本文探讨了在机器学习过程中常被忽略的数据预处理步骤 —— 数据填补，对医疗领域存在的偏见和影响较大的群体不平等性产生的影响，并提出了缓解不平等的建议。