In studies of discrimination, one popular strategy is to estimate disparities after adjusting for observed covariates, typically with a…
The field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last decade, several formal,…
Automated Court Date Reminders Reduce Warrants for Arrest: Evidence from a Text Messaging Experiment
Millions of people in the U.S. every year are required to attend mandatory court dates as their cases proceed through the criminal legal…
People with important evidence-based ideas often struggle to translate data into stories their readers can relate to and understand. And if…
Past studies have found that racial and ethnic minorities are more likely than White drivers to be pulled over by the police for alleged…
In domains ranging from computer vision to natural language processing, machine learning models have been shown to exhibit stark…
We describe and apply three empirical approaches to identify superfluous police activity, unjustified racially disparate impacts, and…
In the dominant paradigm for designing equitable machine learning systems, one works to ensure that model predictions satisfy various…
Many scholars, engineers, and policymakers believe that algorithmic fairness requires disregarding information about certain…
Jurisdictions across the country, including the federal government through its recently enacted First Step Act, have begun using…