17 citations · 58 across the 9 of their papers we have counts for
10 papers
A structured regression approach for evaluating model performance across intersectional subgroups
Christine Herlihy, Kimberly Truong, Alexandra Chouldechova +1
Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups defined by combinations of…
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…
Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints
Jamelle Watson-Daniels, Solon Barocas, Jake M. Hofman +1
Prediction models have been widely adopted as the basis for decision-making in domains as diverse as employment, education, lending, and health. Yet, few real world problems readil…
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…
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…
Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models
Emily Black, Hadi Elzayn, Alexandra Chouldechova +2
This study examines issues of algorithmic fairness in the context of systems that inform tax audit selection by the United States Internal Revenue Service (IRS). While the field of…
Characterizing Fairness Over the Set of Good Models Under Selective Labels
Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova
Algorithmic risk assessments are used to inform decisions in a wide variety of high-stakes settings. Often multiple predictive models deliver similar overall performance but differ…
Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy
Amanda Coston, Neel Guha, Derek Ouyang +3
Anonymized smartphone-based mobility data has been widely adopted in devising and evaluating COVID-19 response strategies such as the targeting of public health resources. Yet litt…
Counterfactual Predictions under Runtime Confounding
Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova
Algorithms are commonly used to predict outcomes under a particular decision or intervention, such as predicting whether an offender will succeed on parole if placed under minimal…
Counterfactual Risk Assessments, Evaluation, and Fairness
Amanda Coston, Alan Mishler, Edward H. Kennedy +1
Algorithmic risk assessments are increasingly used to help humans make decisions in high-stakes settings, such as medicine, criminal justice and education. In each of these cases,…