Mapping Discrimination in LLM-Driven HR Systems

The United Nations’ Sustainable Development Goals (UN SDGs) prioritise inclusive and fair employment. However, AI-powered recruitment tools—particularly Large Language Models (LLMs)—raise concerns about potential demographic bias. This paper presents a controlled synthetic dataset and methodology to measure how sensitive attributes (e.g., race, gender, age) influence candidate rankings and pairwise comparisons in LLM-based hiring pipelines. Specifically, we generated a balanced dataset of 1,000 synthetic candidate profiles (each including a cover letter) and evaluated it using 28 frontier LLMs, including proprietary (e.g., OpenAI GPT, Gemini, Grok, Claude) and opensource (e.g., Llama, GigaChat) models. Synthetic data eliminates real-world demographic/occupational confounders, ensuring observed disparities reflect only LLMs’ intrinsic behaviour. Results show professional attributes (e.g., skills, experience) are primary ranking drivers, with 76%–80% statistically significant; however, 8%–9% of demographic attributes exhibit persistent, significant biases across multiple LLMs.We develop a “bias map” quantifying LLM performance, emphasising that mitigating even minor biases in automated hiring is critical to avoid perpetuating employment inequities and uphold the UN SDGs’ inclusive vision.