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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
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