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 this https URL .

我们研究在具有预测不确定性的公平机器学习中，以实现可靠和可信赖的决策。我们提出了一种新的基于不确定性的公平性方法 —— Equal Opportunity of Coverage (EOC)，旨在实现两个性质：（1）相似结果的不同组别的覆盖率相近，（2）整个人群的覆盖率保持在预定水平。我们通过一个无分布的后处理方法，称为 Binned Fair Quantile Regression (BFQR)，来改进 EOC，该方法为任何训练的机器学习模型提供合理的宽度，我们的实验结果表明了我们方法在改进 EOC 方面的有效性。