  Fairness in machine learning (ML) is an ever-growing field of research due to
the manifold potential for harm from algorithmic discrimination. To prevent
such harm, a large body of literature develops new approaches to quantify
fairness. Here, we investigate how one can divert the quantification of
fairness by describing a practice we call "fairness hacking" for the purpose of
shrouding unfairness in algorithms. This impacts end-users who rely on learning
algorithms, as well as the broader community interested in fair AI practices.
We introduce two different categories of fairness hacking in reference to the
established concept of p-hacking. The first category, intra-metric fairness
hacking, describes the misuse of a particular metric by adding or removing
sensitive attributes from the analysis. In this context, countermeasures that
have been developed to prevent or reduce p-hacking can be applied to similarly
prevent or reduce fairness hacking. The second category of fairness hacking is
inter-metric fairness hacking. Inter-metric fairness hacking is the search for
a specific fair metric with given attributes. We argue that countermeasures to
prevent or reduce inter-metric fairness hacking are still in their infancy.
Finally, we demonstrate both types of fairness hacking using real datasets. Our
paper intends to serve as a guidance for discussions within the fair ML
community to prevent or reduce the misuse of fairness metrics, and thus reduce
overall harm from ML applications.
