709 citations · 710 across the 4 of their papers we have counts for
4 papers
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting
Preethi Lahoti, Nicholas Blumm, Xiao Ma +8
A crucial challenge for generative large language models (LLMs) is diversity: when a user's prompt is under-specified, models may follow implicit assumptions while generating a res…
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Flavien Prost, Ben Packer, Jilin Chen +9
There has been a flurry of research in recent years on notions of fairness in ranking and recommender systems, particularly on how to evaluate if a recommender allocates exposure e…
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective
Flavien Prost, Pranjal Awasthi, Nick Blumm +7
In this work we study the problem of measuring the fairness of a machine learning model under noisy information. Focusing on group fairness metrics, we investigate the particular b…
A dynamic network approach for the study of human phenotypes
Cesar A. Hidalgo, Nicholas Blumm, Albert-Laszlo Barabasi +1
The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here w…