16 citations · 38 across the 11 of their papers we have counts for
5 papers · 1 filter
A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing
Gabriel Agostini, Emma Pierson, Nikhil Garg
Decision-makers often observe the occurrence of events through a reporting process. City governments, for example, rely on resident reports to find and then resolve urban infrastru…
Domain constraints improve risk prediction when outcome data is missing
Sidhika Balachandar, Nikhil Garg, Emma Pierson
Machine learning models are often trained to predict the outcome resulting from a human decision. For example, if a doctor decides to test a patient for disease, will the patient t…
Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers
Rajiv Movva, Sidhika Balachandar, Kenny Peng +3
Large language models (LLMs) are dramatically influencing AI research, spurring discussions on what has changed so far and how to shape the field's future. To clarify such question…
Detecting disparities in police deployments using dashcam data
Matt Franchi, J. D. Zamfirescu-Pereira, Wendy Ju +1
Large-scale policing data is vital for detecting inequity in police behavior and policing algorithms. However, one important type of policing data remains largely unavailable withi…
Coarse race data conceals disparities in clinical risk score performance
Rajiv Movva, Divya Shanmugam, Kaihua Hou +4
Healthcare data in the United States often records only a patient's coarse race group: for example, both Indian and Chinese patients are typically coded as "Asian." It is unknown,…