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Mitigating Bias in Retail AI Hiring Tools: A Human-Centered H-A-I-R-E Governance Framework for Low-Wage Hires

Year: 2026
Type: conference-paper
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.In addition, existing research on bias in AI recruitment tools, external vendor practices, and their impact on retail hiring was reviewed.These methods were used to assess transparency, human oversight, and decision-making practices when third-party hiring tools are used.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 engineering 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.The research shows that a human-centered framework, such as H-A-I-R-E, may reduce discrimination, build trust among candidates, and promote fairness by controlling AI tools, especially for low-wage hires.Algorithms-centric hiring tools are engineered sociotechnical systems.Also, the proposed framework may improve ethical and effective recruitment.Although retail is the focus of this study, the framework is designed to apply to other industries that have a similar approach in hiring large numbers, such as hospitality, logistics, and healthcare, for lowwage hires.
DOI: 10.18260/1-2--58593
Language: English
Open Access status: bronze
OpenAlex ID: W7157600772
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