The counterfactuals were generated using seven iterations, each consisting of a batch of 1,000 samples for measurements and counterfactuals. The decrease of accepted solutions with each iteration results from an increase in model robustness and a proof that the generating algorithm finds fewer solutions or the found solutions have too many changes to be accepted. To evaluate the model fairness update, a benchmark dataset was used after each new batch of synthetic data was pushed back into the training dataset, and the model was updated. After each benchmark, we extracted the number of times each feature was changed for both generated and accepted solutions. For the initial iteration, Gender was the primary feature that was changed to generate a potential solution (696 potential solutions, 302 passing the threshold). As the model is updated and the fairness is corrected, later iterations observed a decrease in solutions with changes in Gender and mainly focused on other, more relevant to the real-world features, such as Age, Education Num and Hours per Week.