Fairness in machine learning is an important and popular topic these days. “Fair” machine learning approaches are supposed to produce decisions that are probabilistically independent of sensitive features (such as gender and race) or their proxies (such as zip codes). Some examples of probabilistically fair measures here include precision parity, true positive parity, and false positive parity across pre-defined groups in the population (e.g., whites vs. non-whites). Most literature in this area frame the machine learning problem as estimating a risk score. For example, Jack’s risk of defaulting on a loan is 8, while Jill's is 2. Recent papers - by Kleinberg, Mullainathan, and Raghavan (arXiv:1609.05807v2, 2016) and Alexandra Chouldechova (arXiv:1703.00056v1 , 2017) - present an impossibility result on simultaneously satisfying three desirable fairness properties when estimating risk scores with differing base rates in the population. I take a boarder notion of fairness and ask the following two questions: Is there such a thing as just machine learning? If so, is just machine learning possible in our unjust world? I will describe a different way of framing the problem and will present some preliminary results.
Prof. Tina Eliassi-Rad, Ph.D., is an Associate Professor of Computer Science at Northeastern University in Boston, MA. She is also on the faculty of Northeastern's Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of massive data from networked representations of physical and social phenomena. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, and cyber situational awareness. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). She received an Outstanding Mentor Award from the Office of Science at the US Department of Energy.