Abstract:
In this paper, we consider the problem of batch classifcation and propose a novel framework for
achieving fairness in such settings. The problem of batch classifcation involves selection of a set of
individuals, often encountered in real-world scenarios such as job recruitment, college admissions etc.
This is in contrast to a typical classifcation problem, where each candidate in the test set is considered
separately and independently. In such scenarios, achieving the same acceptance rate (i.e., probability
of the classifer assigning positive class) for each group (membership determined by the value of
sensitive attributes such as gender, race etc.) is often not desirable, and the regulatory body specifes
a diferent acceptance rate for each group. The existing fairness enhancing methods do not allow for
such specifcations and hence are unsuited for such scenarios. In this paper, we defne a confguration
model whereby the acceptance rate of each group can be regulated and further introduce a novel
batch-wise fairness post-processing framework using the classifer confdence-scores. We deploy our
framework across four real-world datasets and two popular notions of fairness, namely demographic
parity and equalized odds. In addition to consistent performance improvements over the competing
baselines, the proposed framework allows fexibility and signifcant speed-up. It can also seamlessly
incorporate multiple overlapping sensitive attributes. To further demonstrate the generalizability of
our framework, we deploy it to the problem of fair gerrymandering where it achieves a better fairnessaccuracy trade-of than the existing baseline method.
