Health Data Science Seminar: Dr. Debarghya Mukherjee, Boston University.

Talk title: “Learning From Data: Two Aspects of Fairness” As modern machine learning systems increasingly influence high-stakes decision-making in areas such as hiring, lending, and law enforcement, ensuring fairness in these automated processes is critical. This presentation explores two key aspects of fairness—group fairness and individual fairness—and discusses their theoretical foundations, challenges, and practical implementations. We highlight how biases emerge in learned systems due to historical data imbalances and cognitive biases and talk about some strategies to mitigate them at different stages of model development, from data collection to post-deployment auditing. Finally, we explore several promising directions for advancing fairness in AI systems, highlighting potential contributions that can help develop more equitable and accountable models in the future.