AI-Based Recruitment Systems and Language Bias: Implications for Human Resource Management and Applied Linguistics

Abstract

The modern advancements in artificial intelligence (AI) technology have led to a shift in human resource management due to processes such as automated hiring of candidates, semantic matching, and evidence-based decision-making. In turn, their dependence on linguistic information triggers certain concerns about language biases, algorithmic fairness, transparency, and diversity in recruiting. The present research is dedicated to the perspectives of AI recruiting and the connection between concepts such as language bias, applied linguistics, algorithmic fairness, transparency, candidate trust, and diversity in HRM. The methodological framework is based on the application of quantitative research and the collection of information via an online survey with 15 research statements (five-point Likert scale) and six sample characteristics from 213 participants. Descriptive statistics, reliability analysis, the Spearman-Brown procedure for two-item constructs, and Pearson correlation analysis will be used. The results have indicated a relatively positive attitude towards AI-based recruiting, with Inclusive HRM showing the highest construct mean (M = 4.01), followed by Transparency and Candidate Trust (M = 3.93). Language bias demonstrated high internal consistency (α=0.85), while AI recruitment yielded α=0.78. The two-item constructs evaluated via the Spearman–Brown procedure demonstrated robust reliability, with Inclusive HRM achieving ????sb=0.81, Applied Linguistics ????sb=0.79, and Algorithmic Fairness ????sb=0.71. The strongest correlation (r = 0.479) was identified between algorithmic fairness and transparency, highlighting the necessity to pay attention to both. The results prove the significance of language diversity considerations, algorithmic transparency, fairness assessment, candidate trust, and human control for developing an inclusive recruiting process with the help of AI.