The Price of Diversity
It addresses bias in critical societal decisions like parole and lending, offering an interpretable tool for human decision-makers, though it is incremental in applying existing optimization techniques to this problem.
The paper tackles systemic bias in selection processes by proposing an optimization method to flip outcome labels and train classifiers, aiming to enhance diversity without significantly harming meritocracy. It demonstrates on parole, bar admissions, and lending datasets that the 'price of diversity' is low or negative, sometimes improving meritocracy.
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.