466 citations · 532 across the 10 of their papers we have counts for
12 papers
CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies
Brian M Cho, Ana-Roxana Pop, Kyra Gan +4
When modifying existing policies in high-risk settings, it is often necessary to ensure with high certainty that the newly proposed policy improves upon a baseline, such as the sta…
Matched Pair Calibration for Ranking Fairness
Hannah Korevaar, Chris McConnell, Edmund Tong +6
We propose a test of fairness in score-based ranking systems called matched pair calibration. Our approach constructs a set of matched item pairs with minimal confounding differenc…
Adaptive Sampling Strategies to Construct Equitable Training Datasets
William Cai, Ro Encarnacion, Bobbie Chern +4
In domains ranging from computer vision to natural language processing, machine learning models have been shown to exhibit stark disparities, often performing worse for members of…
Two-sided fairness in rankings via Lorenz dominance
Virginie Do, Sam Corbett-Davies, Jamal Atif +1
We consider the problem of generating rankings that are fair towards both users and item producers in recommender systems. We address both usual recommendation (e.g., of music or m…
Online certification of preference-based fairness for personalized recommender systems
Virginie Do, Sam Corbett-Davies, Jamal Atif +1
Recommender systems are facing scrutiny because of their growing impact on the opportunities we have access to. Current audits for fairness are limited to coarse-grained parity ass…
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
Chloé Bakalar, Renata Barreto, Stevie Bergman +13
Many technical approaches have been proposed for ensuring that decisions made by machine learning systems are fair, but few of these proposals have been stress-tested in real-world…
Mitigating Included- and Omitted-Variable Bias in Estimates of Disparate Impact
Jongbin Jung, Sam Corbett-Davies, Johann D. Gaebler +2
Managers, employers, policymakers, and others often seek to understand whether decisions are biased against certain groups. One popular analytic strategy is to estimate disparities…
The Measure and Mismeasure of Fairness
Sam Corbett-Davies, Johann D. Gaebler, Hamed Nilforoshan +2
The field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last decade, several formal, mathematical definitions of fairness have…
A large-scale analysis of racial disparities in police stops across the United States
Emma Pierson, Camelia Simoiu, Jan Overgoor +4
To assess racial disparities in police interactions with the public, we compiled and analyzed a dataset detailing over 60 million state patrol stops conducted in 20 U.S. states bet…
Fast Threshold Tests for Detecting Discrimination
Emma Pierson, Sam Corbett-Davies, Sharad Goel
Threshold tests have recently been proposed as a useful method for detecting bias in lending, hiring, and policing decisions. For example, in the case of credit extensions, these t…
Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller +2
Algorithms are now regularly used to decide whether defendants awaiting trial are too dangerous to be released back into the community. In some cases, black defendants are substant…
The Problem of Infra-marginality in Outcome Tests for Discrimination
Camelia Simoiu, Sam Corbett-Davies, Sharad Goel
Outcome tests are a popular method for detecting bias in lending, hiring, and policing decisions. These tests operate by comparing the success rate of decisions across groups. For…