Title: Discriminatory Transfer,
Abstract: We observe standard transfer learning can improve prediction accuracies of
target tasks at the cost of lowering their prediction fairness -- a phenomenon
we named discriminatory transfer. We examine prediction fairness of a standard
hypothesis transfer algorithm and a standard multi-task learning algorithm, and
show they both suffer discriminatory transfer on the real-world Communities and
Crime data set. The presented case study introduces an interaction between
fairness and transfer learning, as an extension of existing fairness studies
that focus on single task learning.