The susceptibility to biases and discrimination is a pressing issue in today's labor markets. While digital recruitment systems play an increasingly significant role in human resource management, a systematic understanding of human-centered design principles for fair online hiring remains lacking, particularly considering the gap between idealized conceptualizations of fairness in research and actual fairness concerns expressed by job seekers. To address this gap, this work explores the potential of developing a fair recruitment framework based on job seekers' fairness concerns shared in r/jobs, one of the largest online job communities. Through a grounded theory approach, we uncover four overarching themes of job seekers' fairness concerns: personal attribute discrimination beyond legally protected attributes, interaction biases, improper interpretations of qualifications, and power imbalance. Drawing on value sensitive design, we derive design implications for fair algorithms and interfaces in recruitment systems, integrating them into a conceptual framework that spans different hiring stages.

翻译：招聘系统中的偏见与歧视问题在当今劳动力市场中日益突出。尽管数字化招聘系统在人力资源管理中发挥着越来越重要的作用，但对于实现公平在线招聘的人本设计原则，目前仍缺乏系统性的理解，尤其是在研究中对公平的理想化概念与求职者实际表达的公平关切之间存在显著差距。为填补这一空白，本研究探讨了基于求职者在全球最大在线求职社区之一 r/jobs 中分享的公平关切，构建公平招聘框架的可能性。通过扎根理论方法，我们提炼出求职者公平关切的四大主题：超越法律保护属性的个人特征歧视、交互偏见、对资历的不当解读以及权力失衡。借鉴价值敏感设计理论，我们推导出招聘系统中公平算法与界面的设计启示，并将其整合为一个覆盖不同招聘阶段的概念框架。