Navigating a Black Box: Students’ Experiences and Perceptions of Automated Hiring

Abstract

Automated hiring algorithms are increasingly used in computing job recruitment. Prior work has examined perceptions of algorithmic fairness and established bias in hiring algorithms, but there is limited work on the ability of computer science students, who are applying for their first computing job, to overcome new barriers posed by automated hiring. To investigate what challenges students face, how they work through them, and their perceptions of these systems, we conducted semi-structured interviews with post-secondary students who were first-time computing job applicants. Analyses revealed that participants had diverse knowledge of hiring algorithms; some people knew to use strategies, such as keywords in resumes, online assessment practice, and referrals to circumvent automated processes to progress to in-person interviews, but others were entirely unaware of the automation. Participants also expressed that current systems prevented them from demonstrating the full extent of their skills and attributed job offers to personal contacts within the company. While some deemed automation a "necessary evil" to combat scale, many struggled with the inequity automated hiring processes perpetuated. Understanding student experiences and perspectives with automated hiring has relevance for how current computer science curricula prepares students for the transition to computing jobs post-graduation. Our findings have implications for how to develop new practices to better support students in their transitions amid a changing hiring landscape.

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