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@INPROCEEDINGS{Li:910372,
      author       = {Li, Jingwei and Bzdok, Danilo and Tam, Angela and Ooi, Leon
                      Qi Rong and Holmes, Avram and Ge, Tian and Patil, Kaustubh
                      and Jabbi, Mbemba and Eickhoff, Simon and Yeo, Thomas and
                      GENON, Sarah},
      title        = {{C}ross-ethnicity/race generalization failure of
                      {RSFC}-based behavioral prediction and potential
                      consequences},
      reportid     = {FZJ-2022-03783},
      year         = {2022},
      abstract     = {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.},
      month         = {Oct},
      date          = {2022-10-18},
      organization  = {INM $\&$ IBI Retreat 2022 "Molecular
                       neuroscience meets brain function",
                       Jülich (Germany), 18 Oct 2022 - 19 Oct
                       2022},
      subtyp        = {Other},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/910372},
}