  Advances in data analytics bring with them civil rights implications.
Data-driven and algorithmic decision making increasingly determine how
businesses target advertisements to consumers, how police departments monitor
individuals or groups, how banks decide who gets a loan and who does not, how
employers hire, how colleges and universities make admissions and financial aid
decisions, and much more. As data-driven decisions increasingly affect every
corner of our lives, there is an urgent need to ensure they do not become
instruments of discrimination, barriers to equality, threats to social justice,
and sources of unfairness. In this paper, we argue for a concrete research
agenda aimed at addressing these concerns, comprising five areas of emphasis:
(i) Determining if models and modeling procedures exhibit objectionable bias;
(ii) Building awareness of fairness into machine learning methods; (iii)
Improving the transparency and control of data- and model-driven decision
making; (iv) Looking beyond the algorithm(s) for sources of bias and
unfairness-in the myriad human decisions made during the problem formulation
and modeling process; and (v) Supporting the cross-disciplinary scholarship
necessary to do all of that well.
