  A central goal of algorithmic fairness is to reduce bias in automated
decision making. An unavoidable tension exists between accuracy gains obtained
by using sensitive information (e.g., gender or ethnic group) as part of a
statistical model, and any commitment to protect these characteristics. Often,
due to biases present in the data, using the sensitive information in the
functional form of a classifier improves classification accuracy. In this paper
we show how it is possible to get the best of both worlds: optimize model
accuracy and fairness without explicitly using the sensitive feature in the
functional form of the model, thereby treating different individuals equally.
Our method is based on two key ideas. On the one hand, we propose to use
Multitask Learning (MTL), enhanced with fairness constraints, to jointly learn
group specific classifiers that leverage information between sensitive groups.
On the other hand, since learning group specific models might not be permitted,
we propose to first predict the sensitive features by any learning method and
then to use the predicted sensitive feature to train MTL with fairness
constraints. This enables us to tackle fairness with a three-pronged approach,
that is, by increasing accuracy on each group, enforcing measures of fairness
during training, and protecting sensitive information during testing.
Experimental results on two real datasets support our proposal, showing
substantial improvements in both accuracy and fairness.
