  The fairness in machine learning is getting increasing attention, as its
applications in different fields continue to expand and diversify. To mitigate
the discriminated model behaviors between different demographic groups, we
introduce a novel post-processing method to optimize over multiple fairness
constraints through group-aware threshold adaptation. We propose to learn
adaptive classification thresholds for each demographic group by optimizing the
confusion matrix estimated from the probability distribution of a
classification model output. As we only need an estimated probability
distribution of model output instead of the classification model structure, our
post-processing model can be applied to a wide range of classification models
and improve fairness in a model-agnostic manner and ensure privacy. This even
allows us to post-process existing fairness methods to further improve the
trade-off between accuracy and fairness. Moreover, our model has low
computational cost. We provide rigorous theoretical analysis on the convergence
of our optimization algorithm and the trade-off between accuracy and fairness
of our method. Our method theoretically enables a better upper bound in near
optimality than existing method under same condition. Experimental results
demonstrate that our method outperforms state-of-the-art methods and obtains
the result that is closest to the theoretical accuracy-fairness trade-off
boundary.
