  Individual fairness is an intuitive definition of algorithmic fairness that
addresses some of the drawbacks of group fairness. Despite its benefits, it
depends on a task specific fair metric that encodes our intuition of what is
fair and unfair for the ML task at hand, and the lack of a widely accepted fair
metric for many ML tasks is the main barrier to broader adoption of individual
fairness. In this paper, we present two simple ways to learn fair metrics from
a variety of data types. We show empirically that fair training with the
learned metrics leads to improved fairness on three machine learning tasks
susceptible to gender and racial biases. We also provide theoretical guarantees
on the statistical performance of both approaches.

Machine Learning;Machine Learning;Methodology