17 citations · 58 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…