  Systemic bias with respect to gender, race and ethnicity, often unconscious,
is prevalent in datasets involving choices among individuals. Consequently,
society has found it challenging to alleviate bias and achieve diversity in a
way that maintains meritocracy in such settings. We propose (a) a novel
optimization approach based on optimally flipping outcome labels and training
classification models simultaneously to discover changes to be made in the
selection process so as to achieve diversity without significantly affecting
meritocracy, and (b) a novel implementation tool employing optimal
classification trees to provide insights on which attributes of individuals
lead to flipping of their labels, and to help make changes in the current
selection processes in a manner understandable by human decision makers. We
present case studies on three real-world datasets consisting of parole,
admissions to the bar and lending decisions, and demonstrate that the price of
diversity is low and sometimes negative, that is we can modify our selection
processes in a way that enhances diversity without affecting meritocracy
significantly, and sometimes improving it.
