发表机构
University of Bordeaux; IAE（波尔多大学; 企业管理学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该论文结合多视角研究AutoML在人力资源招聘中的公平性，发现现有平台存在缺陷，提出五维度HCI公平评估框架，建议将公平性嵌入AutoML设计以提升伦理可持续性。
AI 中文摘要
本论文从法规、商业战略与人机交互（HCI）相结合的视角，研究人力资源招聘系统中自动机器学习（AutoML）工具的公平性问题。论文指出，公平性不再仅仅是伦理问题，更是可用性、信任、合规性与组织采用的关键决定因素。AutoML平台虽通过简化模型选择与部署提升效率，但在基于存在偏见的历史招聘数据训练时，仍存在延续歧视性结果的风险。现有平台优先考虑技术性能而非公平性，导致非专业业务用户无法有效检测或缓解偏见。本研究围绕公平机制、界面透明度、人工监督与产品设计优先级四个研究问题，探究AutoML工具的公平性缺口。研究运用技术接受模型、创新扩散理论、以人为中心的人工智能、认知负荷理论与可供性理论等框架，评估可用性与公平性的契合度。通过对8款AutoML平台在人力资源数据集上的定性HCI审计与定量测试，研究发现这些平台在透明度、用户控制与偏见缓解支持方面存在普遍缺陷。论文提出了一个基于HCI的五维度公平性评估框架，并建议将公平性直接嵌入AutoML产品设计，以提升AI驱动招聘系统的问责性、采用率与伦理可持续性。
英文摘要
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.
Comments295 pages; Doctoral Thesis;28 figures; 42 tables; 248 references
