The value of prediction in identifying the worst-off: Interview with Unai Fischer Abaigar

At this year's International Conference on Machine Learning (ICML2025), Unai Fischer-Abaigar, Christoph Kern and Juan Carlos Perdomo won an outstanding paper award for their work The Value of Prediction in Identifying the Worst-Off. We hear from Unai about the main contributions of the paper, why prediction systems are an interesting area for study, and further work they are planning in this space. My work focuses on prediction systems used in public institutions to make high-stakes decisions about people. A central example is resource allocation, where institutions face limited capacity and must decide which cases to prioritize. Think of an employment office deciding which jobseekers are most at risk of long-term unemployment, a hospital triaging patients, or fraud investigators identifying cases most likely to warrant investigations.