  Recent research has shown that seemingly fair machine learning models, when
used to inform decisions that have an impact on peoples' lives or well-being
(e.g., applications involving education, employment, and lending), can
inadvertently increase social inequality in the long term. This is because
prior fairness-aware algorithms only consider static fairness constraints, such
as equal opportunity or demographic parity. However, enforcing constraints of
this type may result in models that have negative long-term impact on
disadvantaged individuals and communities. We introduce ELF (Enforcing
Long-term Fairness), the first classification algorithm that provides
high-confidence fairness guarantees in terms of long-term, or delayed, impact.
We prove that the probability that ELF returns an unfair solution is less than
a user-specified tolerance and that (under mild assumptions), given sufficient
training data, ELF is able to find and return a fair solution if one exists. We
show experimentally that our algorithm can successfully mitigate long-term
unfairness.
