Equal Opportunity of Coverage in Fair Regression
Fangxin Wang Lu Cheng Ruocheng Guo Kay Liu Philip S. Yu
We study fair machine learning (ML) under predictive uncertainty to enable
reliable and trustworthy decision-making. The seminal work of ``equalized
coverage'' proposed an uncertainty-aware fairness notion. However, it does not
guarantee equal coverage rates across more fine-grained groups (e.g.,
low-income females) conditioning on the true label and is biased in the
assessment of uncertainty. To tackle these limitations, we propose a new
uncertainty-aware fairness -- Equal Opportunity of Coverage (EOC) -- that aims
to achieve two properties: (1) coverage rates for different groups with similar
outcomes are close, and (2) the coverage rate for the entire population remains
at a predetermined level. Further, the prediction intervals should be narrow to
be informative. We propose Binned Fair Quantile Regression (BFQR), a
distribution-free post-processing method to improve EOC with reasonable width
for any trained ML models. It first calibrates a hold-out set to bound
deviation from EOC, then leverages conformal prediction to maintain EOC on a
test set, meanwhile optimizing prediction interval width. Experimental results
demonstrate the effectiveness of our method in improving EOC. Our code is
publicly available at https://github.com/fangxin-wang/bfqr .