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About
Machine learning models are often assessed by the quality of their predictions, yet their real-world impact extends far beyond these metrics. Models function as interventions within complex social systems, influencing stakeholders, infrastructure, and decision-making processes. This workshop challenges the prediction-centric paradigm, bringing together an interdisciplinary community to explore alternative frameworks that integrate statistical modeling and tools, policy intervention design, and evaluation methodologies. By shifting toward an intervention-based perspective, we aim to foster new methods and theories that bridge machine learning and real-world deployment in social systems.
Chairs/Organizers
Invited Participants
Marc Aidinoff (Harvard University), Jackie Baek (NYU Stern), Hamsa Bastani (Upenn), Emma Brunskill (Stanford University), Sanmay Das (Virginia Tech), Nikhil Garg (Cornell University), Talia Gillis (Columbia University), Sharad Goel (Harvard University), Shion Guha (University of Toronto), Daniel Ho (Stanford University), Jessica Hullman (Northwestern University), Kosuke Imai (Harvard University), Frauke Kreuter (LMU Munich and University of Maryland), Hannah Li (Columbia University), Lydia Liu (Princeton University), Daniel Malinsky (Columbia University), Ashesh Rambachan (MIT), Aaron Roth (University of Pennsylvania), Matthew Salganik (Princeton University), Jann Spiess (Stanford University), Berk Ustun (University of California, San Diego), Brian Wilder (Carnegie Mellon University), Ashia Wilson (MIT), Simone Zhang (New York University), Angela Zhou (University of Southern California), Tijana Zrnic (Stanford University)