Automated interviewing tools are now widely adopted to manage recruitment at scale, often replacing early human screening with algorithmic assessments. While these systems are promoted as efficient and consistent, they also generate new forms of uncertainty for applicants. Efforts to soften these experiences through human-like design features have only partially addressed underlying concerns. To understand how candidates interpret and cope with such systems, we conducted a mixed empirical investigation that combined analysis of online discussions, responses from more than one hundred and fifty survey participants, and follow-up conversations with seventeen interviewees. The findings point to several recurring problems, including unclear evaluation criteria, limited organizational responsibility for automated outcomes, and a lack of practical support for preparation. Many participants described the technology as far less advanced than advertised, leading them to infer how decisions might be made in the absence of guidance. This speculation often intensified stress and emotional strain. Furthermore, the minimal sense of interpersonal engagement contributed to feelings of detachment and disposability. Based on these observations, we propose design directions aimed at improving clarity, accountability, and candidate support in AI-mediated hiring processes.

翻译：自动化面试工具现已广泛应用于大规模招聘管理，通常以算法评估取代早期人工筛选。尽管这些系统被宣传为高效且一致，它们也为求职者带来了新的不确定性形式。通过拟人化设计特征来缓和这些体验的努力仅部分解决了根本关切。为理解求职者如何解读并应对此类系统，我们开展了一项混合实证研究，结合分析了在线讨论、超过150名调查参与者的回应，以及与17位受访者的后续访谈。研究结果揭示了若干反复出现的问题，包括评估标准不明确、组织对自动化结果的责任有限，以及缺乏实用的准备支持。许多参与者认为该技术远未达到宣传的先进程度，导致他们在缺乏指导的情况下推断决策可能如何形成。这种推测往往加剧了压力与情绪负担。此外，人际互动感的缺失加剧了疏离感与可弃置感。基于这些观察，我们提出了旨在提升AI中介招聘流程清晰度、问责制与求职者支持的设计方向。