Mitigating Bias in Retail AI Hiring Tools: A Human-Centered H-A-I-R-E Governance Framework for Low-Wage Hires
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Authors
Chopra, Yatin
Issue Date
2026-04-17
Type
Other
Language
en_US
Keywords
H-A-I-R-E Engineering Framework , Human-Centered Recruitment , Ethical Recruitment , Bias
Alternative Title
Abstract
Retail companies increasingly use AI hiring tools to improve efficiency in high-volume recruitment for low-wage and entry-level positions. These tools support tasks such as screening, shortlisting, video interviews, and offer roll-out. While such systems can reduce turnaround time, prior research suggests that their routine use may introduce bias, particularly when hiring decisions rely heavily on automated outputs. As a result, organizations face a growing need for reliable and consistent governance frameworks to guide the use of AI in hiring. This study is framed as exploratory qualitative research and incorporates human intervention to examine how AI hiring systems are designed, governed, and implemented. Data were collected through semi-structured interviews and surveys with HR managers, frontline or customer-facing workers, and supervisors, along with an inspection of the algorithm-based hiring system and associated recruitment processes. The analysis identified gaps in governance related to transparency, accountability, and human oversight in AI-supported hiring. Based on these findings, the H-A-I-R-E framework (Human Oversight, Accountability, Inclusivity, Responsibility, and Ethics) was developed to support organizations in maintaining human review, conducting audits, ensuring inclusive data practices, and promoting ethical hiring decisions.
Description
UB Rise 2026
The Ernest C. Trefz School of Business