Subgroup Performance

Subgroup performance analysis in machine learning focuses on identifying and mitigating disparities in model accuracy across different population subgroups, addressing concerns of fairness and reliability. Current research investigates the causes of these performance gaps, employing techniques like contrastive learning, slice discovery methods, and distributionally robust optimization to improve model robustness and reduce bias stemming from factors such as data underrepresentation or spurious correlations. These efforts are crucial for ensuring the responsible deployment of machine learning systems in high-stakes applications like healthcare and speech recognition, where equitable performance across all user groups is paramount.