Recommendations in high-stakes settings

Nikhil Garg (Cornell Tech)

Recommendations in high-stakes settings

Recommendation and search systems are now used in high-stakes settings, including to help find jobs, schools, and partners. Building public interest recommender systems in such settings brings both individual-level (enabling exploration, diversity, data quality) and societal (fairness, capacity constraints, algorithmic monoculture) challenges. In this talk, I'll discuss our theoretical, empirical, and deployment work in tackling these challenges, including ongoing work on (a) applicant behavior and recommendations for the NYC HS match, (b) a platform to help discharge patients to long-term care facilities, (c) feed ranking algorithms on Bluesky for research paper recommendations, including the design of steerable and interpretable recommender systems.