Group Disparity

Group disparity research focuses on identifying and mitigating unfairness in automated decision-making systems where different demographic groups experience unequal outcomes. Current research emphasizes developing robust methods for measuring and analyzing these disparities, including causal analysis to understand underlying mechanisms and the development of fairness-aware algorithms that minimize group-level performance differences. This work is crucial for ensuring fairness and accountability in AI systems across various applications, impacting fields from criminal justice to loan applications, and promoting the development of more equitable technologies. A key challenge lies in balancing the need for accurate predictions with the goal of achieving fairness across groups, a trade-off that is actively being investigated.