March 15, 2024, 4:42 a.m. | Axel Abels, Elias Fernandez Domingos, Ann Now\'e, Tom Lenaerts

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.08829v1 Announce Type: cross
Abstract: Individual and social biases undermine the effectiveness of human advisers by inducing judgment errors which can disadvantage protected groups. In this paper, we study the influence these biases can have in the pervasive problem of fake news by evaluating human participants' capacity to identify false headlines. By focusing on headlines involving sensitive characteristics, we gather a comprehensive dataset to explore how human responses are shaped by their biases. Our analysis reveals recurring individual biases and …

abstract arxiv biases capacity collective cs.hc cs.lg cs.si decision errors face fake fake news human influence judgment making paper performance social study type undermine

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

Research Scientist, Demography and Survey Science, University Grad

@ Meta | Menlo Park, CA | New York City

Computer Vision Engineer, XR

@ Meta | Burlingame, CA