The Unequal Opportunities of Large Language Models: Revealing
Demographic Bias through Job Recommendations
- URL: http://arxiv.org/abs/2308.02053v2
- Date: Tue, 9 Jan 2024 07:45:37 GMT
- Title: The Unequal Opportunities of Large Language Models: Revealing
Demographic Bias through Job Recommendations
- Authors: Abel Salinas, Parth Vipul Shah, Yuzhong Huang, Robert McCormack, Fred
Morstatter
- Abstract summary: We propose a simple method for analyzing and comparing demographic bias in Large Language Models (LLMs)
We demonstrate the effectiveness of our method by measuring intersectional biases within ChatGPT and LLaMA.
We identify distinct biases in both models toward various demographic identities, such as both models consistently suggesting low-paying jobs for Mexican workers.
- Score: 5.898806397015801
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 identities, such as
both models consistently suggesting low-paying jobs for Mexican workers or
preferring to recommend secretarial roles to women. Our study highlights the
importance of measuring the bias of LLMs in downstream applications to
understand the potential for harm and inequitable outcomes.
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