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

By Emma Pierson, Camelia Simoiu, Jan Overgoor, Sam Corbett-Davies, Daniel Jenson, Amy Shoemaker, Vignesh Ramachandran, Phoebe Barghouty, Cheryl Phillips, Ravi Shroff and 1 more. 2020

449 citations recorded by the source corpus.

Subfields: Health, Political Science and International Relations, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health

1. Sam Corbett-Davies
2. Sharad Goel
3. Emma Pierson

In Political Science and International Relations

1. Sam Corbett-Davies
2. Emma Pierson
3. Sharad Goel

In Sociology and Political Science

1. Sam Corbett-Davies
2. Emma Pierson
3. Sharad Goel
4. Karthikeyan Natesan Ramamurthy
5. Kush R. Varshney

Citations in the corpus

• AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias (2018, background): “Figure 3 illustrates feature bias localization on the Stanford Open Policing dataset (Pierson et al., 2017) for Connecticut, with county name as the feature and race as the protected attribute.”
• Fast Threshold Tests for Detecting Discrimination (2017, background): “Though still relatively new, the test has already been used to analyze tens of millions of police stops across the United States (Pierson et al., 2017).”
• The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning (2018, methodology): “The figure was generated by assuming risk scores follow a discriminant distribution (Pierson et al., 2018), a type of logit-normal mixture that is well-suited to modeling risk distributions because it is naturally parameterized in terms of its mean and AUC.”