Disparate Impact

Disparate impact refers to the unequal effects of algorithms or systems across different demographic groups, often disproportionately harming minority populations. Current research focuses on identifying and mitigating this unfairness in various contexts, including machine learning models (e.g., using techniques like robust clustering in federated learning and constrained optimization in model pruning), differentially private mechanisms (exploring stratification and improved gradient handling), and even the interpretation of model outputs (analyzing the fairness of explanations themselves). Understanding and addressing disparate impact is crucial for ensuring equitable outcomes in numerous applications, from loan applications and recidivism prediction to content moderation and public health interventions.