Procedural Justice and Fairness in Automated Resume Parsers for Tech Hiring: Insights from Candidate Perspectives

Abstract

To streamline tech hiring processes, the use of talent management platforms has emerged as a new norm. AI-driven Automated Resume Parsers (ARPs) are aimed to simplify the application process for candidates and employers. However, ARP designs typically prioritize employers over candidates. Further, prior work also demonstrates these AI systems are not able to achieve the intended goals of inclusivity and fairness for candidates, negatively impacting minorities in the tech hiring pipeline. Thus, aspiring IT professionals on the job market often spend significant time and effort preparing applications, only to have their resume rejected by an AI model without receiving attention from a recruiter. This work aims to study candidates' perspectives of ARPs. We sent an survey, receiving responses from 103 undergraduate and graduate CS students, and analyze their perspectives through a prism of procedural justice, a measure of fairness. By introducing procedural justice and opting for a human-centered design approach, we believe AI models in the hiring pipeline can achieve the intended goals of inclusivity and fairness. The findings from this study will be beneficial for future designs of more transparent and fair ARPs.

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