WHEN FAIR AI BECOMES UNFAIR: A COUNTERFACTUAL AUDIT OF POSITIONAL BIAS IN LARGE LANGUAGE MODELS FOR HIRING DECISIONS

As Large Language Models (LLMs) are increasingly integrated into high-stakes recruitment processes, rigorous audits to detect algorithmic bias have become critical. This study implements a multi-phase auditing protocol to evaluate gender, racial/ethnic, and positional bias in state-of-the-art proprietary models (gpt-4.1-mini, GPT-5.2, Claude Sonnet 4.6, Gemini 2.5 Pro) and in an exploratory lower-capacity model. Using a simulated CEO-selection scenario with 60 functionally identical profiles, we employ counterfactual testing to isolate the effects of demographic attributes and presentation order. The results reveal a sharp divergence in model behaviour. Frontier models demonstrated remarkable neutrality, with no statistically significant evidence of gender or racial bias. In contrast, the exploratory model exhibited extreme segregation, ranking all female candidates above all male candidates in the original condition (Cliff's δ = 1.0, p < .001). Counterfactual analysis revealed that the root cause was not gender bias per se but an overwhelming primacy bias, whereby candidates presented at the beginning of the prompt were disproportionately favoured. Claude Sonnet 4.6 additionally displayed a form of "conscientious objection", declining to differentiate identical profiles. We complement the experimental audit with an ecosystem-level analysis of 2,653 provider-model listings from the open Models.dev catalogue, which corroborates the open-weights contamination-surface hypothesis but rejects the assumption that systemic monoculture exposure is confined to open-source models. These findings indicate that (a) state-of-the-art LLMs can achieve a high degree of demographic neutrality; (b) fundamental artefacts such as positional bias can nonetheless produce severely discriminatory outcomes; and (c) bias auditing must extend beyond demographic parity to interaction artefacts and ecosystem structure.