activity
20162024
most citedFair prediction with disparate impact: A study of bias in recidivism prediction instruments

19 citations · 38 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

The Impact of Differential Feature Under-reporting on Algorithmic Fairness

Nil-Jana Akpinar, Zachary C. Lipton, Alexandra Chouldechova

Predictive risk models in the public sector are commonly developed using administrative data that is more complete for subpopulations that more greatly rely on public services. In…

stat.ME20232 cited

Estimating the Likelihood of Arrest from Police Records in Presence of Unreported Crimes

Riccardo Fogliato, Arun Kumar Kuchibhotla, Zachary Lipton +3

Many important policy decisions concerning policing hinge on our understanding of how likely various criminal offenses are to result in arrests. Since many crimes are never reporte…

cs.CL202316 cited

Examining risks of racial biases in NLP tools for child protective services

Anjalie Field, Amanda Coston, Nupoor Gandhi +4

Although much literature has established the presence of demographic bias in natural language processing (NLP) models, most work relies on curated bias metrics that may not be refl…

cs.HC20231 cited

Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control

Lingwei Cheng, Alexandra Chouldechova

Algorithm aversion occurs when humans are reluctant to use algorithms despite their superior performance. Studies show that giving users outcome control by providing agency over ho…

stat.AP201619 cited

Fair prediction with disparate impact: A study of bias in recidivism prediction instruments

Alexandra Chouldechova

Recidivism prediction instruments provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instrum…