Algorithmic barriers: Investigating student perceptions of AI bias in subjective “culture fit” hiring

The growing adoption of artificial intelligence (AI) in corporate hiring creates serious ethical concerns because it evaluates subjective parameters like “culture fit”. One particular concern is that AI recruitment platforms, tasked with assessing whether a candidate's values align with a company's culture, may reinforce existing biases through pattern replication and amplification. While marketed as objective, these platforms learn from data containing embedded discriminatory patterns, creating feedback loops that systematically disadvantage underrepresented groups. Drawing on institutional isomorphism theory, prior research suggests that organizations that adopt these technologies are often driven by legitimacy-seeking rather than demonstrated fairness. We hypothesized that high school and college students would express significant concerns about AI-based culture fit assessments, perceiving these systems as likely to reinforce existing biases and systematically disadvantage marginalized applicants in hiring decisions. Our hypothesis was supported by our survey of 150 high school and early-college students in Kathmandu Valley, Nepal, revealing widespread awareness and concern about algorithmic bias among emerging workforce participants. Binary logistic regression analysis found that ethnicity significantly predicts concern about AI bias. Our research shows that emerging workforce participants perceive AI-driven hiring as a potential source of systematic barriers to workplace equity, particularly for neurodivergent, disabled, and culturally diverse applicants, highlighting the need for robust ethical oversight.