Paper Type

Complete

Abstract

With the fast integration of AI into hiring practices, understanding how key stakeholders perceive and adapt to algorithmic systems has become increasingly critical. This comparative analysis of public discourse on AI-driven hiring across three subreddits (r/jobs, r/recruitinghell, and r/programming) reflects the perceptions of job seekers, HR staff, and IT developers. The triangulation of BERTopic modeling of 5,000 comments and qualitative analysis of 250 comments reveals how different stakeholders perceive and adapt to algorithmic hiring systems. Findings highlight: 1) an “AI versus AI battlefield,” where job seekers deploy GenAI to navigate automated screening systems; and 2) algorithmic ambivalence dominates but varies across communities: job seekers exhibit exhaustion and profound distrust, HR staff balance efficiency with ethical strain, and IT developers maintain pragmatic skepticism, while the broader public expresses economic cynicism. Rather than eliminating bias, algorithmic hiring reshapes the process as a visibility contest, transforming merit into algorithmic compatibility.

Paper Number

1198

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Aug 15th, 12:00 AM

Divergent Perceptions of AI in Hiring: Perspectives from Job Seekers, HR Professionals, and IT Developers

With the fast integration of AI into hiring practices, understanding how key stakeholders perceive and adapt to algorithmic systems has become increasingly critical. This comparative analysis of public discourse on AI-driven hiring across three subreddits (r/jobs, r/recruitinghell, and r/programming) reflects the perceptions of job seekers, HR staff, and IT developers. The triangulation of BERTopic modeling of 5,000 comments and qualitative analysis of 250 comments reveals how different stakeholders perceive and adapt to algorithmic hiring systems. Findings highlight: 1) an “AI versus AI battlefield,” where job seekers deploy GenAI to navigate automated screening systems; and 2) algorithmic ambivalence dominates but varies across communities: job seekers exhibit exhaustion and profound distrust, HR staff balance efficiency with ethical strain, and IT developers maintain pragmatic skepticism, while the broader public expresses economic cynicism. Rather than eliminating bias, algorithmic hiring reshapes the process as a visibility contest, transforming merit into algorithmic compatibility.