Abstract
While the literature has explored algorithmic hiring biases (e.g., Parasurama & Sedoc, 2022), our study investigates the impact of job posting disclosures on potential applicants’ perceptions of hiring organizations and interest in posted information technology (IT) positions. Instead of viewing online disclosures as part of a privacy calculus (Li et al., 2010) or as information risk (Wang et al., 2013), we seek to understand the impact of fairness and transparency disclosures with algorithmic IT hiring practices on the reputation of IT employers. Our experimental design randomly assigned participants (N=200) to view one of three job postings for the same IT position, each varying in fairness and transparency signals. Three experiment treatments are used: Treatment 1 posting presented a straightforward list of job responsibilities and requirements (aligned to the candidate’s background), Treatment 2 posting included the exact job details but additionally mentioned the use of algorithmic hiring processes (adding transparency), Treatment 3 lists the posting including transparency signals (noting the use of algorithmic hiring) and includes a statement regarding the company’s commitment to fairness and transparency in their hiring processes. After viewing the job posting, participants were asked to rate their interest in applying to the position and company, thus addressing the core research question: How do different levels of fairness and transparency influence applicant perceptions of positions and employers? We aim to contribute to understanding how transparency in hiring practices, specifically algorithmic hiring, affects IT job seeker behavior. Initial results reveal significant variations in applicant interest based on the level of detail regarding hiring practices. Our research highlights the importance of transparency in recruitment processes, regardless of algorithmic hiring practices. Our findings reveal potential ethical implications and applicant trust issues associated with automated hiring systems for difficult-to-fill IT positions.
Recommended Citation
Thepsouvan, Natacha; Erskine, Michael A.; and Greer, Timothy, "Overcoming Negative Perceptions of Algorithmic IT Hiring Through Fairness and Transparency Disclosures" (2024). AMCIS 2024 TREOs. 81.
https://aisel.aisnet.org/treos_amcis2024/81