  The paper is devoted to the study of the model fairness and process fairness
of the Russian demographic dataset by making predictions of divorce of the 1st
marriage, religiosity, 1st employment and completion of education. Our goal was
to make classifiers more equitable by reducing their reliance on sensitive
features while increasing or at least maintaining their accuracy. We took
inspiration from "dropout" techniques in neural-based approaches and suggested
a model that uses "feature drop-out" to address process fairness. To evaluate a
classifier's fairness and decide the sensitive features to eliminate, we used
"LIME Explanations". This results in a pool of classifiers due to feature
dropout whose ensemble has been shown to be less reliant on sensitive features
and to have improved or no effect on accuracy. Our empirical study was
performed on four families of classifiers (Logistic Regression, Random Forest,
Bagging, and Adaboost) and carried out on real-life dataset (Russian
demographic data derived from Generations and Gender Survey), and it showed
that all of the models became less dependent on sensitive features (such as
gender, breakup of the 1st partnership, 1st partnership, etc.) and showed
improvements or no impact on accuracy
