Machine Learning Models: Bias Mitigation Strategies - DZone AI

In this post, you will learn about some of the bias mitigation strategies that can be applied in ML Model Development lifecycle (MDLC) to achieve discrimination-aware Machine Learning models. The primary objective is to achieve a higher accuracy model while ensuring that the models are lesser discriminant in relation to sensitive/protected attributes. In simple words, the output of the classifier should not correlate with protected or sensitive attributes. Building such ML models becomes the multi-objective optimization problem. The quality of the classifier is measured by its accuracy and the discrimination it makes on the basis of sensitive attributes; the more accurate, the better, and the less discriminant (based on sensitive attributes), the better.