Recommendation systems are now used in high-stakes settings, including to help find jobs, schools, and partners. Building public interest recommender systems in such settings bring both individual-level (enabling exploration, diversity, data quality) and societal (fairness, capacity constraints, monoculture) challenges.
The talk will describe an ongoing collaboration with the NYC Public Schools, in which we designed and deployed an informational intervention to help students from underserved middle schools discover high-performing, nearby high schools where they have a strong individual admissions likelihood. However, recommending specific programs brings a methodological challenge: if many applicants are recommended the same program, affecting admissions likelihoods, then the recommendations may be self-defeating.
Time permitting, the talk will overview other directions in tackling such challenges, including on (a) algorithmic monoculture and LLM homogeneity, (b) a platform to help discharge patients to long-term care facilities, (c) feed ranking algorithms on Bluesky for research paper recommendations.
Bio:
Nikhil Garg is an assistant professor of Operations Research and Information Engineering at Cornell Tech as part of the Jacobs Institute. He uses algorithms, data science, and economics approaches to study democracy, markets, and societal systems at large. Nikhil has received the NSF CAREER, INFORMS George Dantzig Dissertation Award, an honorable mention for the ACM SIGecom dissertation award, and paper awards including from CSCW, EAAMO, and CHIL. He received his Ph.D. from Stanford University and has spent considerable collaborating with government agencies and non-profits. His work has been supported by the NSF, NASA, Sloan Foundation, and other organizations.
In-person attendance is open to Princeton University faculty, staff and students.
This talk will be livestreamed and recorded. The recording will be posted to the CITP website, the Princeton University Media Central channel and the CITP YouTube channel.
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