A Rule Learning Approach to Discovering Contexts of Discrimination

Abstract

In fairness-aware data mining, discrimination discovery refers to determining if social discrimination against certain individuals or groups of individuals exists in labeled data sets or in learned models. In this thesis, I focus on the problem of discovering contexts or niches of discrimination in data sets, i.e. revealing groups of features in a given data set that, when considered together, have a greater degree of discriminatory influence in the data than when any one feature is examined individually. Our approach to this problem involves using the CN2 and CN2-SD rule learning algorithms to identify groups in the data that have signifcant predictive ability, and then using the Gradient Feature Algorithm to quantify and examine each group's discrimination potential. This approach shows promising results for identifying contexts of discrimination.