  Large Language Models (LLMs) are increasingly integrated into critical
decision-making processes, such as loan approvals and visa applications, where
inherent biases can lead to discriminatory outcomes. In this paper, we examine
the nuanced relationship between demographic attributes and socioeconomic
biases in LLMs, a crucial yet understudied area of fairness in LLMs. We
introduce a novel dataset of one million English sentences to systematically
quantify socioeconomic biases across various demographic groups. Our findings
reveal pervasive socioeconomic biases in both established models such as GPT-2
and state-of-the-art models like Llama 2 and Falcon. We demonstrate that these
biases are significantly amplified when considering intersectionality, with
LLMs exhibiting a remarkable capacity to extract multiple demographic
attributes from names and then correlate them with specific socioeconomic
biases. This research highlights the urgent necessity for proactive and robust
bias mitigation techniques to safeguard against discriminatory outcomes when
deploying these powerful models in critical real-world applications.
