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

2025-02-07

AI Summary

This study investigates young technical job seekers’ perceptions of fairness and behavioral responses to automated employment decision tools (AEDTs), challenging the assumption of algorithmic neutrality in hiring. Method: A mixed-methods study was conducted with 448 computer science students, integrating surveys, regression modeling, a validated fairness scale, and systematic coding of job-search strategies. Contribution/Results: The study identifies a differentiated trust mechanism: structural advantages—such as family income and employee referrals—significantly increase perceived and actual hiring success, whereas universalist strategies—e.g., coding practice and keyword optimization—show no measurable effect. Moreover, higher levels of automation in evaluation significantly reduce perceptions of procedural fairness. Critically, findings demonstrate that AEDTs do not mitigate but rather reproduce and amplify preexisting structural inequities. These results provide empirical grounding for rethinking technical ethics and regulatory frameworks governing algorithmic hiring fairness.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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."

Problem

• Perceptions of automated hiring tools
• Strategies for navigating AEDTs
• Impact of social privilege on job success

Innovation

• survey on AEDT perceptions
• analyzed procedural fairness
• explored hiring strategy impacts