A Designed Look at Artificial Intelligence from the Lens of Fairness

As the use of Artificial Intelligence (AI), especially Generative AI, becomes ubiquitous, we take a look at the performance of these methods. We specifically focus on concept of fairness element of trustworthiness. We use Statistical Parity Difference and Equalized Odds Difference to mathematically measure fairness. To systematically study how various factors like bias, access to protected categories, types of intervention affect fairness and accuracy, we performed a simulation as a multi-factor experiment. Our results indicate that accuracy and fairness (in terms of statistical parity and equalized odds) tend to go in opposite directions. This opens up the question of whether we can look at methods that can consider both accuracy and fairness simultaneously.