text: 74,59,1518618467598946304,1870088930,Nikhil Garg,"New paper! ""Equity in Resident Crowdsourcing: Measuring Under-reporting without Ground Truth Data"" w/ @ZhiLiu724  We develop method to quantify under-reporting in @nyc311-like systems. Applying it to NYC Parks data reveals big spatial disparities  <LINK> <LINK> Quantifying under-reporting is tricky. Why? The whole reason these systems exist is bc we don't know when/that incidents happen. So, if something isn't reported: is it because it didn't happen, or because no one reported it? Standard approach: Try to estimate ground truth. This is hard (and harder to validate), for same missing data reasons!  Our approach: Leverage that there are sometimes *multiple* reports about the same incident, to disentangle reporting rates from incident occurrence rates. Why does this matter? If there are reporting disparities, that propagates to downstream disparities in what work government agencies do, and when they do it. We’re now working directly with Parks to understand and improve the end-to-end reporting-to-work pipeline. <LINK> We think our method can be almost directly applied to other reporting systems, like software bugs and other agency/311 systems -- please reach out! Paper link: <LINK> (Virtual) talk next week 5/5 at C3ai seminar: <LINK> And (virtual) talk this Wednesday at TOC4Fairness! <LINK>",https://arxiv.org/abs/2204.08620,"Modern city governance relies heavily on crowdsourcing (or ""co-production"") 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 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 $\textit{duplicate}$ 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. ","Equity in Resident Crowdsourcing: Measuring Under-reporting without