A new thesis explores the integration of fairness and user experience in automated machine learning (AutoML) tools specifically for human resources hiring processes. It highlights that while AutoML enhances efficiency, it risks perpetuating bias from historical data. The research, which involved qualitative HCI audits and quantitative testing of eight AutoML platforms, found significant shortcomings in transparency, user control, and bias mitigation. To address these issues, the thesis proposes a new HCI-based fairness evaluation framework and advocates for embedding fairness directly into the product design of AutoML systems to ensure ethical sustainability and broader adoption in AI-driven hiring. AI
IMPACT Proposes a new framework to improve fairness and user trust in AI hiring tools, potentially influencing future AutoML product design.
RANK_REASON Academic paper detailing a new framework for evaluating AI fairness in a specific application domain. [lever_c_demoted from research: ic=1 ai=1.0]
- automated machine learning
- human-centered AI
- human–computer interaction
- human resource management
- Sundaraparipurnan Narayanan
- Technology Acceptance Model
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