A statistical approach to detect sensitive features in a group fairness
  setting
Guilherme Dean Pelegrina Miguel Couceiro Leonardo Tomazeli Duarte
The use of machine learning models in decision support systems with high
societal impact raised concerns about unfair (disparate) results for different
groups of people. When evaluating such unfair decisions, one generally relies
on predefined groups that are determined by a set of features that are
considered sensitive. However, such an approach is subjective and does not
guarantee that these features are the only ones to be considered as sensitive
nor that they entail unfair (disparate) outcomes.
  In this paper, we propose a preprocessing step to address the task of
automatically recognizing sensitive features that does not require a trained
model to verify unfair results. Our proposal is based on the Hilber-Schmidt
independence criterion, which measures the statistical dependence of variable
distributions. We hypothesize that if the dependence between the label vector
and a candidate is high for a sensitive feature, then the information provided
by this feature will entail disparate performance measures between groups. Our
empirical results attest our hypothesis and show that several features
considered as sensitive in the literature do not necessarily entail disparate
(unfair) results.