Learning Fair Representations

Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork
2013 International Conference on Machine Learning  
We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly). We formulate fairness as an optimization problem of finding a good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously
more » ... g any information about membership in the protected group. We show positive results of our algorithm relative to other known techniques, on three datasets. Moreover, we demonstrate several advantages to our approach. First, our intermediate representation can be used for other classification tasks (i.e., transfer learning is possible); secondly, we take a step toward learning a distance metric which can find important dimensions of the data for classification.
dblp:conf/icml/ZemelWSPD13 fatcat:hk2sarwzjjcm5djg4s5u5so44m