  With growing awareness of societal impact of artificial intelligence,
fairness has become an important aspect of machine learning algorithms. The
issue is that human biases towards certain groups of population, defined by
sensitive features like race and gender, are introduced to the training data
through data collection and labeling. Two important directions of fairness
ensuring research have focused on (i) instance weighting in order to decrease
the impact of more biased instances and (ii) adversarial training in order to
construct data representations informative of the target variable, but
uninformative of the sensitive attributes. In this paper we propose a Fair
Adversarial Instance Re-weighting (FAIR) method, which uses adversarial
training to learn instance weighting function that ensures fair predictions.
Merging the two paradigms, it inherits desirable properties from both --
interpretability of reweighting and end-to-end trainability of adversarial
training. We propose four different variants of the method and, among other
things, demonstrate how the method can be cast in a fully probabilistic
framework. Additionally, theoretical analysis of FAIR models' properties have
been studied extensively. We compare FAIR models to 7 other related and
state-of-the-art models and demonstrate that FAIR is able to achieve a better
trade-off between accuracy and unfairness. To the best of our knowledge, this
is the first model that merges reweighting and adversarial approaches by means
of a weighting function that can provide interpretable information about
fairness of individual instances.
