In criminal justice systems, credit markets, employment arenas, higher education admissions processes and even social media networks, data-driven algorithms now drive decision-making in ways that touch our economic, social and civic lives. These software systems rank, classify, associate or filter information, using human-crafted or data-induced rules that allow for consistent treatment across large populations. But while there may be efficiency gains from these techniques, they can also harbor biases against disadvantaged groups or reinforce structural discrimination. In terms of criminal justice, for example, is it fair to make judgments on an individual’s parole based on statistical tendencies measured across a wide group of people? Could discrimination arise from applying a statistical model developed for one state’s population to another, demographically different population?