Fairness-aware learning aims at constructing classifiers that not only make
accurate predictions, but 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 be not representative of the true data distribution. We introduce FLEA, a
filtering-based algorithm that allows the learning system to identify and
suppress those data sources that would have a negative impact on fairness or
accuracy if they were used for training. 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
unreliable data as long as the fraction of affected data sources is less than
half
