arXiv:2608.07477v1 Announce Type: cross
Abstract: This thesis examines the fairness of Automated Machine Learning (AutoML) tools in human resource hiring systems through the combined lenses of regulation, business strategy, and Human-Computer Interaction (HCI). It argues that fairness is no longer merely an ethical concern but a critical determinant of usability, trust, legal compliance, and organizational adoption. While AutoML platforms improve efficiency by simplifying model selection and deployment, they also risk perpetuating discriminatory outcomes when trained on biased historical hiring data. Existing platforms prioritize technical performance over fairness, leaving non-expert business users unable to detect or mitigate bias effectively. The study investigates fairness gaps in AutoML tools through four research questions focused on fairness mechanisms, interface transparency, human oversight, and product design priorities. Drawing on frameworks such as the Technology Acceptance Model, Innovation Diffusion Theory, Human-Centered AI, Cognitive Load Theory, and Affordance Theory, the research evaluates both usability and fairness alignment. Using qualitative HCI audits and quantitative testing of eight AutoML platforms on HR datasets, the findings reveal widespread deficiencies in transparency, user control, and bias mitigation support. The thesis proposes a five-dimensional HCI-based fairness evaluation framework and recommends embedding fairness directly into AutoML product design to improve accountability, adoption, and ethical sustainability in AI-driven hiring systems.
Abstract: This thesis examines the fairness of Automated Machine Learning (AutoML) tools in human resource hiring systems through the combined lenses of regulation, business strategy, and Human-Computer Interaction (HCI). It argues that fairness is no longer merely an ethical concern but a critical determinant of usability, trust, legal compliance, and organizational adoption. While AutoML platforms improve efficiency by simplifying model selection and deployment, they also risk perpetuating discriminatory outcomes when trained on biased historical hiring data. Existing platforms prioritize technical performance over fairness, leaving non-expert business users unable to detect or mitigate bias effectively. The study investigates fairness gaps in AutoML tools through four research questions focused on fairness mechanisms, interface transparency, human oversight, and product design priorities. Drawing on frameworks such as the Technology Acceptance Model, Innovation Diffusion Theory, Human-Centered AI, Cognitive Load Theory, and Affordance Theory, the research evaluates both usability and fairness alignment. Using qualitative HCI audits and quantitative testing of eight AutoML platforms on HR datasets, the findings reveal widespread deficiencies in transparency, user control, and bias mitigation support. The thesis proposes a five-dimensional HCI-based fairness evaluation framework and recommends embedding fairness directly into AutoML product design to improve accountability, adoption, and ethical sustainability in AI-driven hiring systems.
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