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

ABSTRACT: 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 uncer-tainty, 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
adecision-making context. Our measure's main advantage is that it does not
depend on any machine learning prediction model but adistance function. In this
paper, we extend our study by exploring the existence of bias encoded
implicitly in non-protected featuresas 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 operatorsand
distance function that best capture change in the boundary regions.
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 uncer-tainty, 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
adecision-making context. Our measure's main advantage is that it does not
depend on any machine learning prediction model but adistance function. In this
paper, we extend our study by exploring the existence of bias encoded
implicitly in non-protected featuresas 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 operatorsand
distance function that best capture change in the boundary regions.
A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets
http://arxiv.org/abs/2108.09098v2