Abstract

Warning: This paper discusses and contains content that is offensive or upsetting. Large Language Models (LLMs) have seen widespread deployment in various real-world applications. Understanding these biases is crucial to comprehend the potential downstream consequences when using LLMs to make decisions, particularly for historically disadvantaged groups. In this work, we propose a simple method for analyzing and comparing demographic bias in LLMs, through the lens of job recommendations. We demonstrate the effectiveness of our method by measuring intersectional biases within ChatGPT and LLaMA, two cutting-edge LLMs. Our experiments primarily focus on uncovering gender identity and nationality bias; however, our method can be extended to examine biases associated with any intersection of demographic identities. We identify distinct biases in both models toward various demographic …

Date

October 30, 2023

Authors

Abel Salinas, Parth Shah, Yuzhong Huang, Robert McCormack, Fred Morstatter

Book

Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization

Pages

1-15