Fairnessdplab: Diferansiyel mahremiyetin gözetimli yz algoritmalari üzerindeki adalet etkisinin analizi

This study investigates the impact of differential privacy (DP) on fairness in supervised artificial intelligence (AI) algorithms through the development and evaluation of a benchmark platform, FairnessDPLab. The platform systematically examines the interplay between privacy preservation and fairness across three widely studied datasets: Adult, COMPAS, and German Credit. Utilizing both machine learning and deep learning models, the study explores the effects of varying privacy budgets and their implications on various fairness metrics. The findings indicate that the balance between privacy and fairness can vary under different conditions, such as dataset characteristics or model choices. For instance, lower privacy budget values enhance privacy but may lead to undesirable changes in model accuracy and fairness measures. Deep learning models show higher sensitivity to privacy settings than traditional machine learning methods, such as logistic regression and random forests. The study also highlights the importance of carefully selecting privacy metrics, suggesting that they may need to be adapted based on the domain of application. This work contributes to the growing body of literature on equitable AI systems by providing a robust framework for analyzing fairness under differential privacy constraints. The insights derived from this study offer practical guidelines for designing privacy-preserving AI systems that balance utility and fairness in diverse applications.