Fairness and Symbolic Regression

Abstract

For a fair society, decisions impacting people's lives must be taken fairly, irrespective of their protected characteristics. To achieve algorithmic fairness, appropriate measures must guide machine learning methods toward fair(er) decision recommendations. There are numerous fairness metrics for classification; however, for regression the literature is still developing. Most fair machine learning methods combine fairness and error in a single objective. Instead, we apply multi-objective optimization, allowing the fairness metrics to be optimized alongside the error. We develop a transition from classification to regression for symbolic regression by first discretizing the task into a fixed number of classes, progressively increasing their number, and finally by using full regression fairness metrics. We demonstrate the framework on the Law School Admission Council dataset. While fairness objectives do not appear to be significantly different among methods, accuracy is clearly better when applying full regression fairness.