Fair Classification by Direct Intervention on Operating Characteristics

Kevin Jiang, Edgar Dobriban

Keywords: algorithmic fairness; post-processing; linear-fractional constraints; minimal interventions; constrained optimization

Abstract:

We develop new classifiers under group fairness in the attribute-aware setting for binary classification with multiple group fairness constraints (e.g., demographic parity (DP), equalized odds (EO), and predictive parity (PP)).
We propose a novel approach based on directly intervening on the operating characteristics of a pre-trained base classifier, by:
(i) identifying optimal operating characteristics using the base classifier's group-wise ROC convex hulls;
(ii) post-processing the base classifier to match those targets.
As practical post-processors,
we consider randomizing a mixture of group-wise thresholding rules subject to minimizing the expected number of interventions.
We further extend our approach to handle multiple protected attributes and multiple linear fractional constraints.
On standard datasets (COMPAS and ACSIncome),
our method simultaneously
satisfies approximate DP, EO, and PP with few interventions and a nearly optimal drop in accuracy; and compare favorably to previous methods.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 12468