  The ongoing pandemic has highlighted the importance of reliable and efficient
clinical trials in healthcare. Trial sites, where the trials are conducted, are
chosen mainly based on feasibility in terms of medical expertise and access to
a large group of patients. More recently, the issue of diversity and inclusion
in clinical trials is gaining importance. Different patient groups may
experience the effects of a medical drug/ treatment differently and hence need
to be included in the clinical trials. These groups could be based on
ethnicity, co-morbidities, age, or economic factors. Thus, designing a method
for trial site selection that accounts for both feasibility and diversity is a
crucial and urgent goal. In this paper, we formulate this problem as a ranking
problem with fairness constraints. Using principles of fairness in machine
learning, we learn a model that maps a clinical trial description to a ranked
list of potential trial sites. Unlike existing fairness frameworks, the group
membership of each trial site is non-binary: each trial site may have access to
patients from multiple groups. We propose fairness criteria based on
demographic parity to address such a multi-group membership scenario. We test
our method on 480 real-world clinical trials and show that our model results in
a list of potential trial sites that provides access to a diverse set of
patients while also ensuing a high number of enrolled patients.
