Fair Classifiers that Abstain without Harm
Tongxin Yin Jean-François Ton Ruocheng Guo Yuanshun Yao Mingyan Liu Yang Liu
In critical applications, it is vital for classifiers to defer
decision-making to humans. We propose a post-hoc method that makes existing
classifiers selectively abstain from predicting certain samples. Our abstaining
classifier is incentivized to maintain the original accuracy for each
sub-population (i.e. no harm) while achieving a set of group fairness
definitions to a user specified degree. To this end, we design an Integer
Programming (IP) procedure that assigns abstention decisions for each training
sample to satisfy a set of constraints. To generalize the abstaining decisions
to test samples, we then train a surrogate model to learn the abstaining
decisions based on the IP solutions in an end-to-end manner. We analyze the
feasibility of the IP procedure to determine the possible abstention rate for
different levels of unfairness tolerance and accuracy constraint for achieving
no harm. To the best of our knowledge, this work is the first to identify the
theoretical relationships between the constraint parameters and the required
abstention rate. Our theoretical results are important since a high abstention
rate is often infeasible in practice due to a lack of human resources. Our
framework outperforms existing methods in terms of fairness disparity without
sacrificing accuracy at similar abstention rates.