National Origin Discrimination in Deep-learning-powered Automated Resume
  Screening
Sihang Li Kuangzheng Li Haibing Lu
Many companies and organizations have started to use some form of AIenabled
auto mated tools to assist in their hiring process, e.g. screening resumes,
interviewing candi dates, performance evaluation. While those AI tools have
greatly improved human re source operations efficiency and provided
conveniences to job seekers as well, there are increasing concerns on unfair
treatment to candidates, caused by underlying bias in AI systems. Laws around
equal opportunity and fairness, like GDPR, CCPA, are introduced or under
development, in attempt to regulate AI. However, it is difficult to implement
AI regulations in practice, as technologies are constantly advancing and the
risk perti nent to their applications can fail to be recognized. This study
examined deep learning methods, a recent technology breakthrough, with focus on
their application to automated resume screening. One impressive performance of
deep learning methods is the represen tation of individual words as
lowdimensional numerical vectors, called word embedding, which are learned from
aggregated global wordword cooccurrence statistics from a cor pus, like
Wikipedia or Google news. The resulting word representations possess interest
ing linear substructures of the word vector space and have been widely used in
down stream tasks, like resume screening. However, word embedding inherits and
reinforces the stereotyping from the training corpus, as deep learning models
essentially learn a probability distribution of words and their relations from
history data. Our study finds out that if we rely on such deeplearningpowered
automated resume screening tools, it may lead to decisions favoring or
disfavoring certain demographic groups and raise eth ical, even legal,
concerns. To address the issue, we developed bias mitigation method. Extensive
experiments on real candidate resumes are conducted to validate our study