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 a forgotten preprocessing step. At best, practitioners guide imputation choice by optimising overall performance, ignoring how this preprocessing can reinforce 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 in different ways. Finally, we propose recommendations for mitigating inequity stemming 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。

临床存在下的填补策略：对算法公平性的影响