  Machine learning methods based on AdaBoost have been widely applied to
various classification problems across many mission-critical applications
including healthcare, law and finance. However, there is a growing concern
about the unfairness and discrimination of data-driven classification models,
which is inevitable for classical algorithms including AdaBoost. In order to
achieve fair classification, a novel fair AdaBoost (FAB) approach is proposed
that is an interpretable fairness-improving variant of AdaBoost. We mainly
investigate binary classification problems and focus on the fairness of three
different indicators (i.e., accuracy, false positive rate and false negative
rate). By utilizing a fairness-aware reweighting technique for base
classifiers, the proposed FAB approach can achieve fair classification while
maintaining the advantage of AdaBoost with negligible sacrifice of predictive
performance. In addition, a hyperparameter is introduced in FAB to show
preferences for the fairness-accuracy trade-off. An upper bound for the target
loss function that quantifies error rate and unfairness is theoretically
derived for FAB, which provides a strict theoretical support for the
fairness-improving methods designed for AdaBoost. The effectiveness of the
proposed method is demonstrated on three real-world datasets (i.e., Adult,
COMPAS and HSLS) with respect to the three fairness indicators. The results are
accordant with theoretic analyses, and show that (i) FAB significantly improves
classification fairness at a small cost of accuracy compared with AdaBoost; and
(ii) FAB outperforms state-of-the-art fair classification methods including
equalized odds method, exponentiated gradient method, and disparate
mistreatment method in terms of the fairness-accuracy trade-off.
