Incorporating Fairness Measures into Multiobjective Fuzzy Genetics-based Machine Learning

Abstract

In recent years, pattern classification has been used for various real-world problems. However, there may be a bias toward certain attributes in data collection. This may result in inappropriate classification biased toward particular social groups. For example, when designing a classifier that recommends whether an applicant should be hired or not for recruitment problems, there is a possibility that attributes such as race and gender affect hiring outcomes. So far, we have developed multiobjective fuzzy genetics-based machine learning (MoFGBML) considering classification performance and interpretability. This paper incorporates two fairness measures into MoFGBML to design fuzzy classifiers considering classification performance, interpretability, and fairness.