Navigating Automated Hiring: Perceptions, Strategy Use, and Outcomes Among Young Job Seekers

TL;DR

This study explores young computer science job seekers' perceptions of automated hiring tools, their strategies, and how these factors influence their job search success, highlighting distrust in automation and the role of social privilege.

Contribution

It provides new insights into young applicants' perceptions and strategies regarding AEDTs, and how these relate to hiring outcomes in the context of increasing automation.

Findings

Perceptions of fairness vary with automation level and task type.

Referrals and income positively influence hiring success.

Egalitarian strategies did not significantly impact outcomes.

Abstract

As the use of automated employment decision tools (AEDTs) has rapidly increased in hiring contexts, especially for computing jobs, there is still limited work on applicants' perceptions of these emerging tools and their experiences navigating them. To investigate, we conducted a survey with 448 computer science students (young, current technology job-seekers) about perceptions of the procedural fairness of AEDTs, their willingness to be evaluated by different AEDTs, the strategies they use relating to automation in the hiring process, and their job seeking success. We find that young job seekers' procedural fairness perceptions of and willingness to be evaluated by AEDTs varied with the level of automation involved in the AEDT, the technical nature of the task being evaluated, and their own use of strategies, such as job referrals. Examining the relationship of their strategies with job outcomes, notably, we find that referrals and family household income have significant and positive impacts on hiring success, while more egalitarian strategies (using free online coding assessment practice or adding keywords to resumes) did not. Overall, our work speaks to young job seekers' distrust of automation in hiring contexts, as well as the continued role of social and socioeconomic privilege in job seeking, despite the use of AEDTs that promise to make hiring "unbiased."