The need for transparent demographic group trade-offs in Credit Risk and Income Classification
Abstract
Prevalent methodology towards constructing fair machine
learning (ML) systems, is to enforce a strict equality metric for de-
mographic groups based on protected attributes like race and gender.
While definitions of fairness in philosophy are varied, mitigating bias
in ML classifiers often relies on demographic parity-based constraints
across sub-populations. However, enforcing such constraints blindly can
lead to undesirable trade-offs between group-level accuracy if groups pos-
sess different underlying sampled population metrics, an occurrence that
is surprisingly common in real-world applications like credit risk and
income classification. Similarly, attempts to relax hard constraints may
lead to unintentional degradation in classification performance, without
benefit to any demographic group. In these increasingly likely scenar-
ios, we make the case for transparent human intervention in making the
trade-offs between the accuracies of demographic groups. We propose
that transparency in trade-offs between demographic groups should be
a key tenet of ML design and implementation. Our evaluation demon-
strates that a transparent human-in-the-loop trade-off technique based
on the Pareto principle increases both overall and group-level accuracy
by 9.5% and 9.6% respectively, in two commonly explored UCI datasets
for credit risk and income classification.
Research Areas
