Individual Gaps in Prediction Generalization

Explore how machine learning models fail to generalize predictions across different individuals and populations in this 31-minute conference talk that examines the critical gaps between algorithmic predictions and real-world outcomes. Delve into the challenges of prediction generalization when models trained on one population are applied to different demographic groups or individuals, understanding why standard machine learning approaches often produce biased or inaccurate results for underrepresented populations. Learn about the methodological approaches for identifying and measuring these generalization gaps, including techniques for evaluating model performance across different subgroups and understanding the sources of prediction disparities. Discover strategies for improving model fairness and generalization, including approaches for collecting more representative training data, developing robust evaluation metrics, and designing algorithms that account for individual and population-level differences. Examine case studies from healthcare, criminal justice, and other high-stakes domains where prediction gaps can have significant real-world consequences, and understand the ethical implications of deploying models with poor generalization properties. Gain insights into the intersection of machine learning, fairness, and social impact, with particular focus on how technical decisions in model development can perpetuate or mitigate existing inequalities in automated decision-making systems.