000			02103 am a22002413u 4500
042			_adc
100	1	0	_aJeanselme, Vincent _eauthor _91926
700	1	0	_aDe-Arteaga, Maria _eauthor _91927
700	1	0	_aZhang, Zhe _eauthor _91928
700	1	0	_aBarrett, Jessica _eauthor _91929
700	1	0	_aTom, Brian _eauthor _91930
245	0	0	_aImputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness
260			_c2022.
500			_a/pmc/articles/PMC7614014/
520			_aBiases 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 groupspecific 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 groupspecific 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.
540			_a
540			_ahttps://creativecommons.org/licenses/by/4.0/This work is licensed under a CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) International license.
546			_aen
690			_aArticle
655	7		_aText _2local
786	0		_nProc Mach Learn Res
856	4	1	_u/pubmed/36601036 _zConnect to this object online.
999			_c852 _d852