  Addressing fairness concerns about machine learning models is a crucial step
towards their long-term adoption in real-world automated systems. While many
approaches have been developed for training fair models from data, little is
known about the effects of data corruption on these methods. In this work we
consider fairness-aware learning under arbitrary data manipulations. We show
that an adversary can force any learner to return a biased classifier, with or
without degrading accuracy, and that the strength of this bias increases for
learning problems with underrepresented protected groups in the data. We also
provide upper bounds that match these hardness results up to constant factors,
by proving that two natural learning algorithms achieve order-optimal
guarantees in terms of both accuracy and fairness under adversarial data
manipulations.
