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The first step towards reducing the pervasive disparities in women’s health is to quantify them. Accurate estimates of therelative prevalenceacross groups—capturing, for example, that a condition affects Black women more frequently than white women—facilitate effective and equitable health policy that prioritizes groups who are disproportionately affected by a condition. However, it is difficult to estimate relative prevalence when a health condition is underreported, as many women’s health conditions are. In this work, we present , a method for accurately estimating the relative prevalence of underreported health conditions which builds upon the literature in positive unlabeled learning. We show that under a commonly made assumption—that the probability of having a health condition given a set of symptoms remains constant across groups—we can recover the relative prevalence, even without restrictive assumptions commonly made in positive unlabeled learning and even if it is impossible to recover the absolute prevalence. We conduct experiments on synthetic and real health data which demonstrate ’s ability to recover the relative prevalence more accurately than do previous methods. We then use to quantify the relative prevalence of intimate partner violence (IPV) in two large emergency department datasets. We find higher prevalences of IPV among patients who are on Medicaid, not legally married, and non-white, and among patients who live in lower-income zip codes or in metropolitan counties. We show that correcting for underreporting is important to accurately quantify these disparities and that failing to do so yields less plausible estimates. Our method is broadly applicable to underreported conditions in women’s health, as well as to gender biases beyond healthcare.

 

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 within the United States: aggregated police deployment data capturing which neighborhoods have the heaviest police presences. Here we show that disparities in police deployment levels can be quantified by detecting police vehicles in dashcam images of public street scenes. Using a dataset of 24,803,854 dashcam images from rideshare drivers in New York City, we find that police vehicles can be detected with high accuracy (average precision 0.82, AUC 0.99) and identify 233,596 images which contain police vehicles. There is substantial inequality across neighborhoods in police vehicle deployment levels. The neighborhood with the highest deployment levels has almost 20 times higher levels than the neighborhood with the lowest. Two strikingly different types of areas experience high police vehicle deployments — 1) dense, higher-income, commercial areas and 2) lower-income neighborhoods with higher proportions of Black and Hispanic residents. We discuss the implications of these disparities for policing equity and for algorithms trained on policing data. 

Given the impact that medical expenses have, disclosing them should be a part of the informed consent process, argue Leah Pierson and Emma Pierson 

Algorithms provide powerful tools for detecting and dissecting human bias and error. Here, we develop machine learning methods to to analyze how humans err in a particular high-stakes task: image interpretation. We leverage a unique dataset of 16,135,392 human predictions of whether a neighborhood voted for Donald Trump or Joe Biden in the 2020 US election, based on a Google Street View image. We show that by training a machine learning estimator of the Bayes optimal decision for each image, we can provide an actionable decomposition of human error into bias, variance, and noise terms, and further identify specific features (like pickup trucks) which lead humans astray. Our methods can be applied to ensure that human-in-the-loop decision-making is accurate and fair and are also applicable to black-box algorithmic systems.