Fair Forests: Regularized Tree Induction to Minimize Model Bias

Understand

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.