A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets

The need to measure bias encoded in tabular data that are used to solve pattern recognition problems is widely recognized by academia, legislators and enterprises alike. In previous work, we proposed a bias quantification measure, called fuzzy-rough uncertainty, which relies on the fuzzy-rough set theory. The intuition dictates that protected features should not change the fuzzy-rough boundary regions of a decision class significantly. The extent to which this happens is a proxy for bias expressed as uncertainty in a decision-making context. Our measure’s main advantage is that it does not depend on any machine learning prediction model but a distance function. In this paper, we extend our study by exploring the existence of bias encoded implicitly in non-protected features as defined by the correlation between protected and unprotected attributes. This analysis leads to four scenarios that domain experts should evaluate before deciding how to tackle bias. In addition, we conduct a sensitivity analysis to determine the fuzzy operators and distance function that best capture change in the boundary regions.

Publication year:2022

Keywords:Bias, Fairness, Explainable machine learning, Fuzzy-rough sets

Accessibility:Open