Quantifying Algorithmic Friction in Automated Resume Screening Systems

TL;DR

This paper defines and quantifies algorithmic friction in automated resume screening by treating screening as a binary classification problem and measuring the excess false-negative rate caused by representational misinterpretation. Through controlled simulations, it compares traditional keyword-based screening with vector-space semantic matching, showing that semantic representations dramatically reduce false negatives (friction) while maintaining precision. The study frames friction as a system-level property of screening infrastructure, implying that redesigning screening to preserve semantic meaning can improve labor-market matching efficiency at scale. The findings highlight a practical pathway to reduce provisional hiring bottlenecks without relaxing qualification standards, with implications for labor-market dynamics and future empirical work on real-world screening data.

Abstract

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.