TOC4Fairness Seminar – Nikhil Garg

Date: Wednesday, April 27th, 2022
9:00 am – 10:00 am Pacific Time
12:00 pm – 1:00 pm Eastern Time

Location: Weekly Seminar, Zoom

Title:  Auditing and Designing for Equity in Resident Crowdsourcing


Modern city governance relies heavily on crowdsourcing (or “coproduction”) to identify problems such as downed trees and power-lines. A major concern in these systems is that residents do not report problems at the same rates, leading to an inefficient and inequitable allocation of government resources. However, measuring such under-reporting is a difficult statistical task, as, almost by definition, we do not observe incidents that are not reported. Thus, distinguishing between low reporting rates and low ground-truth incident rates is challenging. First, joint with Zhi Liu, we develop a method to identify (heterogeneous) reporting rates, without using external (proxy) ground truth data. Our insight is that rates on duplicate reports about the same incident can be leveraged, to turn the question into a standard Poisson rate estimation task—even though the full incident reporting interval is also unobserved. We apply our method to over 100,000 resident reports made to the New York City Department of Parks and Recreation, finding that there are substantial spatial and socio-economic disparities in reporting rates, even after controlling for incident characteristics. Second, I’ll overview our work in redesigning inspection decisions to improve system efficiency and equity. While much of the talk is empirical, I’ll outline open theoretical questions in mechanism/market design and algorithmic fairness that are inspired by our applied work. 


Nikhil Garg is an Assistant Professor of Operations Research and Information Engineering at Cornell Tech as part of the Jacobs Technion-Cornell Institute. His research interest is the application of algorithms, data science, and mechanism design to the study of democracy, markets, and societal systems at large. He received his PhD from Stanford University and has spent considerable time in industry — most recently, he was the Principal Data Scientist at PredictWise, which provides election analytics for political campaigns. Nikhil received the INFORMS George Dantzig Dissertation Award and an honorable mention for the ACM SIGecom dissertation award.