  Fairness-aware learning aims at constructing classifiers that not only make
accurate predictions, but also do not discriminate against specific groups. It
is a fast-growing area of machine learning with far-reaching societal impact.
However, existing fair learning methods are vulnerable to accidental or
malicious artifacts in the training data, which can cause them to unknowingly
produce unfair classifiers. In this work we address the problem of fair
learning from unreliable training data in the robust multisource setting, where
the available training data comes from multiple sources, a fraction of which
might not be representative of the true data distribution. We introduce FLEA, a
filtering-based algorithm that identifies and suppresses those data sources
that would have a negative impact on fairness or accuracy if they were used for
training. As such, FLEA is not a replacement of prior fairness-aware learning
methods but rather an augmentation that makes any of them robust against
unreliable training data. We show the effectiveness of our approach by a
diverse range of experiments on multiple datasets. Additionally, we prove
formally that -- given enough data -- FLEA protects the learner against
corruptions as long as the fraction of affected data sources is less than half.
Our source code and documentation are available at
https://github.com/ISTAustria-CVML/FLEA.
