  Fairness and Outlier Detection (OD) are closely related, as it is exactly the
goal of OD to spot rare, minority samples in a given population. However, when
being a minority (as defined by protected variables, such as
race/ethnicity/sex/age) does not reflect positive-class membership (such as
criminal/fraud), OD produces unjust outcomes. Surprisingly, fairness-aware OD
has been almost untouched in prior work, as fair machine learning literature
mainly focuses on supervised settings. Our work aims to bridge this gap.
Specifically, we develop desiderata capturing well-motivated fairness criteria
for OD, and systematically formalize the fair OD problem. Further, guided by
our desiderata, we propose FairOD, a fairness-aware outlier detector that has
the following desirable properties: FairOD (1) exhibits treatment parity at
test time, (2) aims to flag equal proportions of samples from all groups (i.e.
obtain group fairness, via statistical parity), and (3) strives to flag truly
high-risk samples within each group. Extensive experiments on a diverse set of
synthetic and real world datasets show that FairOD produces outcomes that are
fair with respect to protected variables, while performing comparable to (and
in some cases, even better than) fairness-agnostic detectors in terms of
detection performance.
