Mitigating the influence of biased training instances without refitting

Abstract

One or more systems, devices, computer program products and/or computer implemented methods of use provided herein relate to a process of mitigating biased training instances associated with a machine learning model without additional refitting of the machine learning model. A system can comprise a memory that stores computer executable components, and a processor that executed the computer executable components stored in the memory, wherein the computer executable components can comprise a training data influence estimation component and an influence mitigation component. The training data influence estimation component can receive a pre-trained machine learning model and calculate a fairness influence score of training instances on group fairness metrics associated with the pre-trained machine learning model. The influence mitigation component can perform post-hoc unfairness mitigation by removing the effect of at least one training instance based on the fairness influence score to mitigate biased training instances without refitting the pre-trained machine learning model.