Date of Award

Winter 1-2-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial/Organizational Psychology (PhD)

Department

Industrial/Organizational Psychology

First Advisor/Committee Member

Paul R. Yost, Ph.D.

Second Advisor/Committee Member

Jorge Lumbreras, Ph.D.

Third Advisor/Committee Member

Neil Morelli, Ph.D.

Keywords

AI Transparency, Perceived Fairness, AI Literacy, Attitudes Towards AI, Selection, Assessment

Abstract

Artificial intelligence is becoming an increasingly prominent feature of modern hiring, creating opportunities to enhance efficiency and consistency in selection. This study examined how AI transparency, AI literacy, and attitudes toward AI shape applicants’ perceptions of fairness when engaging with AI-scored pre-hire assessments. A sample of 290 U.S. adults were randomly assigned to either a high- or low-transparency hiring scenario and then completed measures of AI literacy, attitudes toward AI, and procedural fairness. Results indicated that both AI literacy and attitudes toward AI were strong and significant predictors of perceived procedural fairness, suggesting that applicants who feel knowledgeable about AI and hold more positive views of it respond favorably to AI-enabled assessments. Transparency was not directly related to procedural justice, but demonstrated a significant, although small effect on fairness perceptions when AI literacy and attitudes were included in the model. The hypothesized indirect effects of transparency on perceived fairness through AI literacy and attitudes toward AI were not supported. Overall, the findings highlight several promising pathways for organizations to design AI-assisted selection processes that promote applicant understanding and perceptions of fairness.

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