Automated resume screening systems are now a central part of hiring at scale, yet there is growing evidence that rigid screening logic can exclude qualified candidates before human review. In prior work, we introduced the concept of Artificial Frictional Unemployment to describe labor market inefficiencies arising from automated recruitment systems. This paper extends that framework by focusing on measurement. We present a method for quantifying algorithmic friction in resume screening pipelines by modeling screening as a classification task and defining friction as excess false negative rejection caused by semantic misinterpretation. Using controlled simulations, we compare deterministic keyword-based screening with vector-space semantic matching under identical qualification conditions. The results show that keyword-based screening exhibits high levels of algorithmic friction, while semantic representations substantially reduce false negative rejection without compromising precision. By treating algorithmic friction as a system-level property, this study provides an empirical basis for evaluating how recruitment system design affects matching efficiency in modern labor markets.

翻译：自动简历筛选系统已成为大规模招聘的核心环节，然而越来越多的证据表明，僵化的筛选逻辑可能在人工审核前就已排除合格候选人。在先前研究中，我们引入“人工摩擦性失业”这一概念来描述自动化招聘系统引发的劳动力市场低效现象。本文通过聚焦测量方法拓展该框架，提出一种量化简历筛选流程中算法摩擦的技术：将筛选建模为分类任务，并将摩擦定义为语义误判导致的超额假阴性拒录。通过受控模拟实验，我们在相同资质条件下对比基于关键词的确定性筛选与向量空间语义匹配方法。结果表明：基于关键词的筛选表现出高水平的算法摩擦，而语义表征方法能在保持精度的前提下显著降低假阴性拒录率。通过将算法摩擦视为系统级属性，本研究为评估招聘系统设计如何影响现代劳动力市场匹配效率提供了实证基础。