Mitigating Socio-lingustic Bias in Job Recommendation
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Ketki V. Deshpande et al., Mitigating Socio-lingustic Bias in Job Recommendation, http://jfoulds.informationsystems.umbc.edu/papers/2020/Deshpande%20(2020)%20-%20Mitigating%20Socio-lingustic%20Bias%20in%20Job%20Recommendation%20(MASC-SLL).pdf
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Abstract
With increasing diversity in the job market as well as the workforce, employers receive resumes from an increasingly 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. The resume writing style and socio-linguistics are a potential source of bias as
they correlate with protected characteristics. Studies and field experiments in the past have confirmed the presence of bias in the labor market based on gender, race (Bertrand and Mullainathan, 2004), and ethnicity (Oreopoulos, 2011). A biased
dataset is often translated into biased AI algorithms (Rudinger et al., 2017) and de-biasing algorithms are being contemplated (Bolukbasi et al., 2016). In this work, we aim to identify and mitigate the effects of socio-linguistic bias on resume to job description matching algorithms