Bias and Fairness in LLM-Based Recruitment: A Systematic Review
As large language models emerge as critical infrastructure in labor markets, questions about their governance touch on some of the most contested issues at the intersection of AI development, complex sociotechnical systems and emerging technology regulation. Recruitment tools powered by these models are now widely deployed across industries and they raise urgent concerns about intersectional algorithmic discrimination—patterns of unequal treatment that remain invisible so long as auditors examine only one protected attribute at a time. Trained on historical hiring data, these systems tend to reproduce and in some cases deepen, discriminatory patterns that cut across gender, race and other protected characteristics simultaneously. Two research communities bear on this problem without yet speaking to each other adequately: the fairness-in-NLP literature and the human-centered AI (HCAI) governance literature have each grown substantially, but largely in parallel. To examine where they diverge and why, we conducted a PRISMA 2020-guided systematic literature review drawing on 82 studies selected from 493 records retrieved from Scopus and Web of Science. Grounded theory coding and a concept matrix organize the findings around four thematic axes: bias sources across the hiring pipeline, formal fairness metrics and their mathematical limits, debiasing techniques and governance frameworks. What the concept matrix reveals is, above all, a structural disconnect. Technical studies rarely engage with HCAI design principles governance-oriented work rarely operationalizes the technical limitations that the empirical literature has documented in detail. Five evidence-based design recommendations follow from the analysis; a particularly urgent recommendation concerns the development of non-Western fairness benchmarks. A targeted research agenda addresses intersectional auditing and LLM-specific debiasing as the two highest priority open problems.