Equity in Resident Crowdsourcing: Measuring Under-reporting without Ground Truth Data
Modern city governance relies heavily on crowdsourcing (or “coproduction”) to identify problems such as downed trees and powerlines. A major concern in these systems is that residents do not report problems at the same rates, leading to an 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. 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. Joint work with Zhi Liu.
Bio: Nikhil Garg joined the Cornell University faculty as an Assistant Professor of Operations Research and Information Engineering at Cornell Tech in July 2021.
Garg’s research is at the intersection of computer science, economics, and operations—on the application of algorithms, data science, and mechanism design to the study of democracy, markets, and societal systems at large. His research interests include surge pricing, rating systems, how to vote on budgets, the role of testing in college admissions, stereotypes in word embeddings, and polarization on Twitter.
Garg received his Ph.D. from Stanford University in 2020, where he was part of the Society and Algorithms Lab and Stanford Crowdsourced Democracy Team. He also received a B.S. and B.A. degrees from the University of Texas at Austin in 2015.
He has spent time at Uber, NASA, Microsoft, the Texas Senate, and IEEE’s policy arm, and most recently was the principal data scientist at PredictWise—which provides election analytics for political campaigns—and is currently completing a postdoc at the University of California, Berkeley in the Department of Electrical Engineering and Computer Science.
