Raising Both the Ceiling and the Floor: Mitigating Demographic Bias in AI-based Career Counseling
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Date
2020-01-20
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Department
Information Systems
Program
Information Systems
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Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Abstract
With increasing diversity in the market as well as the work force there is an increasing chance of employers getting resumes from a diverse population. Many employers have started using automated resume screening to filter the many possible matches. Depending on how the automated screening algorithm is trained it may show bias towards a particular population by favoring certain socio-linguistic characteristics. Studies and field experiments in the past have confirmed the presence of bias in the labor market based on gender, race, and ethnicity. A biased dataset is often translated into biased AI algorithms and de-biasing algorithms are being contemplated. In this theses, I analyzed the effects of socio-linguistic bias on resume to job description matching algorithm. I have also developed a simple technique to match resumes with job description in a fairer way by mitigating the socio-linguistic bias.