A Methodology based on Rebalancing Techniques to measure and improve Fairness in Artificial Intelligence algorithms

Artificial Intelligence (AI) has become one of the key drivers for the next decade. As important decisions are increasingly supported or directly made by AI systems, concerns regarding the rationale and fairness in their outputs are becoming more and more prominent nowadays. Following the recent interest in fairer predictions, several metrics for measuring fairness have been proposed, leading to different objectives which may need to be addressed in different fashion. In this paper, we propose (i) a methodology for analyzing and improving fairness in AI predictions by selecting sensitive attributes that should be protected; (ii) We analyze how the most common rebalance approaches affect the fairness of AI predictions and how they compare to the alternatives of removing or creating separate classifiers for each group within a protected attribute. Finally, (iii) our methodology generates a set of tables that can be easily computed for choosing the best alternative in each particular case. The main advantage of our methodology is that it allows AI practitioners to measure and improve fairness in AI algorithms in a systematic way. In order to check our proposal, we have properly applied it to the COMPAS dataset, which has been widely demonstrated to be biased by several previous studies.