TY  - CONF
AU  - Li, Jingwei
AU  - Bzdok, Danilo
AU  - Tam, Angela
AU  - Ooi, Leon Qi Rong
AU  - Holmes, Avram
AU  - Ge, Tian
AU  - Patil, Kaustubh
AU  - Jabbi, Mbemba
AU  - Eickhoff, Simon
AU  - Yeo, Thomas
AU  - GENON, Sarah
TI  - Cross-ethnicity/race generalization failure of RSFC-based behavioral prediction and potential consequences
M1  - FZJ-2022-03783
PY  - 2022
AB  - Machine learning (ML) plays an important role in precision medicine. However, algorithmic biases that favor majority populations pose a key challenge to ML applications (Chouldechova 2018; Martin 2019; Obermeyer 2019). In neuroimaging, there is growing interest in the prediction of behavioral phenotypes based on resting-state functional connectivity (RSFC; Finn 2015, 2021; Greene 2018). But prediction biases/unfairness in this context were not assessed in the literature. Especially, predictive models were typically built by capitalizing on large cohorts with mixed ethnic group, in which the proportions of certain ethnical groups, e.g. African Americans (AA), are limited. Whether the models perform equally well across different ethnic groups was unclear. By using two large-scale neuroimaging datasets from the United States, we compared the prediction accuracy between AA and white Americans (WA) when ML models were trained on different composition of ethnic groups. We observed larger prediction errors in AA than WA for most behavioral measures, which was only limitedly affected by the composition of training population. We also investigated potential downstream consequences of biased predictions of behavioral phenotypes if they were used uncritically.
T2  - INM & IBI Retreat 2022 "Molecular neuroscience meets brain function"
CY  - 18 Oct 2022 - 19 Oct 2022, Jülich (Germany)
Y2  - 18 Oct 2022 - 19 Oct 2022
M2  - Jülich, Germany
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/910372
ER  -