  Devising a fair classifier that does not discriminate against different
groups is an important problem in machine learning. Although researchers have
proposed various ways of defining group fairness, most of them only focused on
the immediate fairness, ignoring the long-term impact of a fair classifier
under the dynamic scenario where each individual can improve its feature over
time. Such dynamic scenarios happen in real world, e.g., college admission and
credit loaning, where each rejected sample makes effort to change its features
to get accepted afterwards. In this dynamic setting, the long-term fairness
should equalize the samples' feature distribution across different groups after
the rejected samples make some effort to improve. In order to promote long-term
fairness, we propose a new fairness notion called Equal Improvability (EI),
which equalizes the potential acceptance rate of the rejected samples across
different groups assuming a bounded level of effort will be spent by each
rejected sample. We analyze the properties of EI and its connections with
existing fairness notions. To find a classifier that satisfies the EI
requirement, we propose and study three different approaches that solve
EI-regularized optimization problems. Through experiments on both synthetic and
real datasets, we demonstrate that the proposed EI-regularized algorithms
encourage us to find a fair classifier in terms of EI. Finally, we provide
experimental results on dynamic scenarios which highlight the advantages of our
EI metric in achieving the long-term fairness. Codes are available in a GitHub
repository, see https://github.com/guldoganozgur/ei_fairness.
