all AI news
Degendering Resumes for Algorithmic Resume Screening. (arXiv:2112.08910v2 [cs.CL] UPDATED)
July 4, 2022, 1:12 a.m. | Prasanna Parasurama, João Sedoc
cs.CL updates on arXiv.org arxiv.org
We investigate whether it is feasible to remove gendered information from
resumes to mitigate potential bias in algorithmic resume screening. Using a
corpus of 709k resumes from IT firms, we first train a series of models to
classify the self-reported gender of the applicant, thereby measuring the
extent and nature of gendered information encoded in resumes. We then conduct a
series of gender obfuscation experiments, where we iteratively remove gendered
information from resumes. Finally, we train a resume screening algorithm …
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
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