  Large participatory biomedical studies, studies that recruit individuals to
join a dataset, are gaining popularity and investment, especially for analysis
by modern AI methods. Because they purposively recruit participants, these
studies are uniquely able to address a lack of historical representation, an
issue that has affected many biomedical datasets. In this work, we define
representativeness as the similarity to a target population distribution of a
set of attributes and our goal is to mirror the U.S. population across
distributions of age, gender, race, and ethnicity. Many participatory studies
recruit at several institutions, so we introduce a computational approach to
adaptively allocate recruitment resources among sites to improve
representativeness. In simulated recruitment of 10,000-participant cohorts from
medical centers in the STAR Clinical Research Network, we show that our
approach yields a more representative cohort than existing baselines. Thus, we
highlight the value of computational modeling in guiding recruitment efforts.
