JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1219 10.6339/26-JDS1219 Statistical Data Science A Designed Look at Artificial Intelligence from the Lens of Fairness https://orcid.org/0000-0001-9458-8447 UddinMd Borhanmdborhan.uddin@wsu.edu1∗ YinMengqi1 DasguptaNairanjana1 Department of Mathematics and Statistics, Washington State University, United States Corresponding author. Email: mdborhan.uddin@wsu.edu. 2026422026241203217 Supplementary Material

The supplementary materials include Data generation process described in 3.2 as well as the full Python code. The Python implementation is also available at Https://github.com/borhan-stat/fairness-simulation-paper.

301120252412026 2026 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2026 Open access article under the CC BY license.

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.

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