  Recent research has helped to cultivate growing awareness that machine
learning systems fueled by big data can create or exacerbate troubling
disparities in society. Much of this research comes from outside of the
practicing data science community, leaving its members with little concrete
guidance to proactively address these concerns. This article introduces issues
of discrimination to the data science community on its own terms. In it, we
tour the familiar data mining process while providing a taxonomy of common
practices that have the potential to produce unintended discrimination. We also
survey how discrimination is commonly measured, and suggest how familiar
development processes can be augmented to mitigate systems' discriminatory
potential. We advocate that data scientists should be intentional about
modeling and reducing discriminatory outcomes. Without doing so, their efforts
will result in perpetuating any systemic discrimination that may exist, but
under a misleading veil of data-driven objectivity.
