The Name Game: Algorithmic Gatekeeping and the Systematic Exclusion of Ethnic Names in Digital Hiring

Algorithmic hiring systems promise to remove human bias from recruitment decisions through objective, data-driven evaluation. This comparative case study challenges such claims by examining how these technologies reproduce and amplify ethnic name discrimination in employment. Drawing on five documented cases from 2018-2024—including Amazon’s failed recruiting algorithm, HireVue’s video assessment platform, and recent large language model studies—this research reveals consistent patterns of algorithmic bias against candidates with non-white ethnic names. The study employs digital stratification theory to analyze how seemingly neutral technologies encode historical inequalities into automated decision-making systems. Findings demonstrate that algorithmic hiring tools discriminate through multiple mechanisms: biased training data that reflects past hiring patterns, natural language processing that associates ethnic names with negative attributes, and multimodal assessment systems that penalize linguistic and cultural differences. Unlike human discrimination, which varies by individual prejudice, algorithmic bias operates with mechanical consistency and scale, affecting millions of job seekers. The research identifies the emergence of “algorithmic capital”—digitally legible characteristics that confer advantages in automated evaluation—as a new form of employment stratification. These systems do not merely replicate human bias; they transform discrimination into a technical process that appears objective while systematically disadvantaging ethnic minorities. The study contributes to critical algorithm studies by documenting how employment discrimination evolves in digital contexts and offers practical recommendations for bias detection and mitigation. As algorithmic hiring becomes standard practice, understanding these discriminatory mechanisms becomes essential for both employment equity and the broader struggle against digital inequality.