  Trustworthy AI is a critical issue in machine learning where, in addition to
training a model that is accurate, one must consider both fair and robust
training in the presence of data bias and poisoning. However, the existing
model fairness techniques mistakenly view poisoned data as an additional bias
to be fixed, resulting in severe performance degradation. To address this
problem, we propose FR-Train, which holistically performs fair and robust model
training. We provide a mutual information-based interpretation of an existing
adversarial training-based fairness-only method, and apply this idea to
architect an additional discriminator that can identify poisoned data using a
clean validation set and reduce its influence. In our experiments, FR-Train
shows almost no decrease in fairness and accuracy in the presence of data
poisoning by both mitigating the bias and defending against poisoning. We also
demonstrate how to construct clean validation sets using crowdsourcing, and
release new benchmark datasets.
