Formal Group Fairness and Accuracy in Automated Decision Making

Most research on fairness in Machine Learning assumes the relationship between and accuracy to be a trade-off, with an increase leading unavoidable loss of accuracy. In this study, several approaches for fair are studied experimentally analyze group fairness. The results indicated that may even benefit each other, which emphasizes importance selecting appropriate measures performance evaluation. This work provides foundation further studies adequate objectives context automated decision making.