Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.

翻译：雇主在招聘全流程中越来越多地采用算法招聘技术。由于该领域涉及高利害关系与结构性不平等，算法公平性在此尤为重要。然而，当前该领域的大多数研究仅提供部分解决方案，通常受限于两种对立叙事：乐观派聚焦于取代有偏见的招聘者决策，悲观派则指向歧视的自动化。算法招聘能否以及更重要的是何种类型算法招聘能比低技术替代方案更少偏见、更有利于社会——这一问题至今悬而未决，损害了该技术的可信度。本跨学科综述面向从业者与研究者，系统且均衡地覆盖了算法招聘与公平性中的系统、偏见、度量指标、缓解策略、数据集及法律维度。通过凸显当前机遇与局限，本工作促进了对该技术的语境化理解与治理，并为未来研究提出建议，以确保所有利益相关者共享收益。