Automating Fairness Configurations for Machine Learning

Recent years have witnessed substantial efforts devoted to ensuring algorithmic fairness for machine learning (ML), spanning from formalizing fairness metrics to designing fairness-enhancing methods. These efforts lead to numerous possible choices in terms of fairness definitions and fairness-enhancing algorithms. However, finding the best fairness configuration (including both fairness definition and fairness-enhancing algorithms) for a specific ML task is extremely challenging in practice. The large design space of fairness configurations combined with the tremendous cost required for fairness deployment poses a major obstacle to this endeavor. This raises an important issue: can we enable automated fairness configurations for a new ML task on a potentially unseen dataset? To this point, we design Auto-Fair, a system that provides recommendations of fairness configurations by ranking all fairness configuration candidates based on their evaluations on prior ML tasks. At the core of Auto-Fair lies a meta-learning model that ranks all fairness configuration candidates by utilizing: (1) a set of meta-features that are derived from both datasets and fairness configurations that were used in prior evaluations; and (2) the knowledge accumulated from previous evaluations of fairness configurations on related ML tasks and datasets. The experimental results on 350 different fairness configurations and 1,500 data samples demonstrate the effectiveness of Auto-Fair.