FAIRNESS IMPROVEMENT THROUGH REINFORCEMENT LEARNING

摘要

A computer-implemented method for improving fairness in a supervised machine-learning model may be provided. The method comprises linking the supervised machine-learning model to a reinforcement learning meta model, selecting a list of hyper-parameters and parameters of the supervised machine-learning model, and controlling at least one aspect of the supervised machine-learning model by adjusting hyper-parameters values and parameter values of the list of hyper-parameters and parameters of the supervised machine-learning model by a reinforcement learning engine relating to the reinforcement learning meta model by calculating a reward function based on multiple conflicting objective functions. The method further comprises repeating iteratively the steps of selecting and controlling for improving a fairness value of the supervised machine-learning model.