Book Description

The issue of socially responsible machine learning has never been more pressing. An entire field of machine learning is dedicated to investigating the societal aspects of automated decision-making systems and providing technical solutions for algorithmic fairness. However, any attempt to improve the fairness of algorithms must be examined under the lens of potential societal harm. In this thesis, we study existing approaches to fair classification and shed light on their various limitations. First, we show that relaxations of fairness constraints used to simplify the learning process of fair models are too coarse, since the final classifier may be distinctly unfair even though the relaxed constraint is satisfied. In response, we propose a new and provably fair method that incorporates the fairness relaxations in a strongly convex formulation. Second, we observe an increased awareness of protected attributes such as race or gender in the last layer of deep neural networks when we regularize them for fair outcomes. Based on this observation, we construct a neural network that explicitly treats input points differently because of protected personal characteristics. With this explicit formulation, we can replicate the predictions of a fair neural network. We argue that both the fair neural network and the explicit formulation demonstrate disparate treatment-a form of discrimination in anti-discrimination laws. Third, we consider fairness properties of the majority vote-a popular ensemble method for aggregating multiple machine learning models to obtain more accurate and robust decisions. We algorithmically investigate worst-case fairness guarantees of the majority vote when it consists of multiple classifiers that are themselves already fair. Under strong independence assumptions on the classifiers, we can guarantee a fair majority vote. Without any assumptions on the classifiers, a fair majority vote cannot be guaranteed in general, but different fairness regimes are possible: on the one hand, using fair classifiers may improve the worst-case fairness guarantees. On the other hand, the majority vote may not be fair at all.