  In recent years, automated data-driven decision-making systems have enjoyed a
tremendous success in a variety of fields (e.g., to make product
recommendations, or to guide the production of entertainment). More recently,
these algorithms are increasingly being used to assist socially sensitive
decision-making (e.g., to decide who to admit into a degree program or to
prioritize individuals for public housing). Yet, these automated tools may
result in discriminative decision-making in the sense that they may treat
individuals unfairly or unequally based on membership to a category or a
minority, resulting in disparate treatment or disparate impact and violating
both moral and ethical standards. This may happen when the training dataset is
itself biased (e.g., if individuals belonging to a particular group have
historically been discriminated upon). However, it may also happen when the
training dataset is unbiased, if the errors made by the system affect
individuals belonging to a category or minority differently (e.g., if
misclassification rates for Blacks are higher than for Whites). In this paper,
we unify the definitions of unfairness across classification and regression. We
propose a versatile mixed-integer optimization framework for learning optimal
and fair decision trees and variants thereof to prevent disparate treatment
and/or disparate impact as appropriate. This translates to a flexible schema
for designing fair and interpretable policies suitable for socially sensitive
decision-making. We conduct extensive computational studies that show that our
framework improves the state-of-the-art in the field (which typically relies on
heuristics) to yield non-discriminative decisions at lower cost to overall
accuracy.
