Fairness of Machine Learning Algorithms in Demography. (arXiv:2202.01013v1 [cs.LG])

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 …

algorithms arxiv fairness learning machine machine learning machine learning algorithms