  Computers are increasingly used to make decisions that have significant
impact in people's lives. Often, these predictions can affect different
population subgroups disproportionately. As a result, the issue of fairness has
received much recent interest, and a number of fairness-enhanced classifiers
and predictors have appeared in the literature. This paper seeks to study the
following questions: how do these different techniques fundamentally compare to
one another, and what accounts for the differences? Specifically, we seek to
bring attention to many under-appreciated aspects of such fairness-enhancing
interventions. Concretely, we present the results of an open benchmark we have
developed that lets us compare a number of different algorithms under a variety
of fairness measures, and a large number of existing datasets. We find that
although different algorithms tend to prefer specific formulations of fairness
preservations, many of these measures strongly correlate with one another. In
addition, we find that fairness-preserving algorithms tend to be sensitive to
fluctuations in dataset composition (simulated in our benchmark by varying
training-test splits), indicating that fairness interventions might be more
brittle than previously thought.
