106 citations · 252 across the 6 of their papers we have counts for
7 papers
Non-Invasive Fairness in Learning through the Lens of Data Drift
Ke Yang, Alexandra Meliou
Machine Learning (ML) models are widely employed to drive many modern data systems. While they are undeniably powerful tools, ML models often demonstrate imbalanced performance and…
Fairness in Ranking: A Survey
Meike Zehlike, Ke Yang, Julia Stoyanovich
In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, inf…
Causal intersectionality for fair ranking
Ke Yang, Joshua R. Loftus, Julia Stoyanovich
In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings. Rankings are used…
Balanced Ranking with Diversity Constraints
Ke Yang, Vasilis Gkatzelis, Julia Stoyanovich
Many set selection and ranking algorithms have recently been enhanced with diversity constraints that aim to explicitly increase representation of historically disadvantaged popula…
A Nutritional Label for Rankings
Ke Yang, Julia Stoyanovich, Abolfazl Asudeh +3
Algorithmic decisions often result in scoring and ranking individuals to determine credit worthiness, qualifications for college admissions and employment, and compatibility as dat…
Measuring Fairness in Ranked Outputs
Ke Yang, Julia Stoyanovich
Ranking and scoring are ubiquitous. We consider the setting in which an institution, called a ranker, evaluates a set of individuals based on demographic, behavioral or other chara…