March 12, 2024, 4:51 a.m. | Amin Abolghasemi, Leif Azzopardi, Arian Askari, Maarten de Rijke, Suzan Verberne

cs.CL updates on arXiv.org arxiv.org

arXiv:2403.05975v1 Announce Type: new
Abstract: In most recent studies, gender bias in document ranking is evaluated with the NFaiRR metric, which measures bias in a ranked list based on an aggregation over the unbiasedness scores of each ranked document. This perspective in measuring the bias of a ranked list has a key limitation: individual documents of a ranked list might be biased while the ranked list as a whole balances the groups' representations. To address this issue, we propose a …

abstract aggregation arxiv bias cs.cl document gender gender bias list measuring perspective ranking studies type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Software Engineering Manager, Generative AI - Characters

@ Meta | Bellevue, WA | Menlo Park, CA | Seattle, WA | New York City | San Francisco, CA

Senior Operations Research Analyst / Predictive Modeler

@ LinQuest | Colorado Springs, Colorado, United States