Title: Fair Forests: Regularized Tree Induction to Minimize Model Bias,
Abstract: The potential lack of fairness in the outputs of machine learning algorithms
has recently gained attention both within the research community as well as in
society more broadly. Surprisingly, there is no prior work developing
tree-induction algorithms for building fair decision trees or fair random
forests. These methods have widespread popularity as they are one of the few to
be simultaneously interpretable, non-linear, and easy-to-use. In this paper we
develop, to our knowledge, the first technique for the induction of fair
decision trees. We show that our "Fair Forest" retains the benefits of the
tree-based approach, while providing both greater accuracy and fairness than
other alternatives, for both "group fairness" and "individual fairness.'" We
also introduce new measures for fairness which are able to handle multinomial
and continues attributes as well as regression problems, as opposed to binary
attributes and labels only. Finally, we demonstrate a new, more robust
evaluation procedure for algorithms that considers the dataset in its entirety
rather than only a specific protected attribute.