  Classifiers and rating scores are prone to implicitly codifying biases, which
may be present in the training data, against protected classes (i.e., age,
gender, or race). So it is important to understand how to design classifiers
and scores that prevent discrimination in predictions. This paper develops
computationally tractable algorithms for designing accurate but fair support
vector machines (SVM's). Our approach imposes a constraint on the covariance
matrices conditioned on each protected class, which leads to a nonconvex
quadratic constraint in the SVM formulation. We develop iterative algorithms to
compute fair linear and kernel SVM's, which solve a sequence of relaxations
constructed using a spectral decomposition of the nonconvex constraint. Its
effectiveness in achieving high prediction accuracy while ensuring fairness is
shown through numerical experiments on several data sets.
