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Open AccessJournal ArticleDOI

Precinct or Prejudice? Understanding Racial Disparities in New York City's Stop-and-Frisk Policy

TLDR
In this article, the authors investigate three million stops in New York City over five years, focusing on cases where officers suspected the stopped individual of criminal possession of a weapon (CPW) and estimate the ex ante probability that the detained suspect has a weapon.
Abstract
Recent studies have examined racial disparities in stop-and-frisk, a widely employed but controversial policing tactic. The statistical evidence, however, has been limited and contradictory. We investigate by analyzing three million stops in New York City over five years, focusing on cases where officers suspected the stopped individual of criminal possession of a weapon (CPW). For each CPW stop, we estimate the ex ante probability that the detained suspect has a weapon. We find that in more than 40% of cases, the likelihood of finding a weapon (typically a knife) was less than 1%, raising concerns that the legal requirement of “reasonable suspicion” was often not met. We further find that blacks and Hispanics were disproportionately stopped in these low hit rate contexts, a phenomenon that we trace to two factors: (1) lower thresholds for stopping individuals — regardless of race — in high-crime, predominately minority areas, particularly public housing; and (2) lower thresholds for stopping minorities relative to similarly situated whites. Finally, we demonstrate that by conducting only the 6% of stops that are statistically most likely to result in weapons seizure, one can both recover the majority of weapons and mitigate racial disparities in who is stopped. We show that this statistically informed stopping strategy can be approximated by simple, easily implemented heuristics with little loss in efficiency.

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Proceedings ArticleDOI

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

TL;DR: A new notion of unfairness, disparate mistreatment, is introduced, defined in terms of misclassification rates, which is proposed for decision boundary-based classifiers and can be easily incorporated into their formulation as convex-concave constraints.
Posted Content

The Impact of Machine Learning on Economics

TL;DR: An assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions, and some highlights from the emerging econometric literature combining machine learning and causal inference.
Journal Article

Fairness Constraints: A Flexible Approach for Fair Classification

TL;DR: A flexible constraint-based framework to enable the design of fair margin-based classifiers and a general and intuitive measure of decision boundary unfairness, which serves as a tractable proxy to several of the most popular computational definitions of unfairness from the literature.
Posted Content

A large-scale analysis of racial disparities in police stops across the United States

TL;DR: It is found that black drivers were less likely to be stopped after sunset, when a ‘veil of darkness’ masks one’s race, suggesting bias in stop decisions and evidence that the bar for searching black and Hispanic drivers was lower than that for searching white drivers.
References
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Book

The Economics of Discrimination

TL;DR: The second edition of "The Economics of Discrimination" has been expanded to include three further discussions of the problem and an entirely new introduction which considers contributions made by others in recent years and some of the more important problems remaining as discussed by the authors.
Journal ArticleDOI

Reasoning the fast and frugal way: models of bounded rationality.

TL;DR: The authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making, and found that these fast and frugal algorithms violate fundamental tenets of classical rationality: they neither look up nor integrate all information.
Journal ArticleDOI

Do we need hundreds of classifiers to solve real world classification problems

TL;DR: The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in theTop-20, respectively).
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