Protected group bias and stereotypes in Large Language Models (2403.14727v1)

Abstract: As modern LLMs shatter many state-of-the-art benchmarks in a variety of domains, this paper investigates their behavior in the domains of ethics and fairness, focusing on protected group bias. We conduct a two-part study: first, we solicit sentence continuations describing the occupations of individuals from different protected groups, including gender, sexuality, religion, and race. Second, we have the model generate stories about individuals who hold different types of occupations. We collect >10k sentence completions made by a publicly available LLM, which we subject to human annotation. We find bias across minoritized groups, but in particular in the domains of gender and sexuality, as well as Western bias, in model generations. The model not only reflects societal biases, but appears to amplify them. The model is additionally overly cautious in replies to queries relating to minoritized groups, providing responses that strongly emphasize diversity and equity to an extent that other group characteristics are overshadowed. This suggests that artificially constraining potentially harmful outputs may itself lead to harm, and should be applied in a careful and controlled manner.

Analyzing Bias in LLMs Across Protected Groups

Introduction

LLMs have become ubiquitous across various domains, aiding in tasks ranging from content generation to customer service. Despite their benefits, concerns about LLMs perpetuating or even amplifying societal biases have persisted. This paper explores bias within LLMs, particularly focusing on protected groups defined by characteristics such as gender, sexuality, religion, and race. By analyzing model outputs for stereotypical content and examining the amplification of bias, the paper contributes to a deeper understanding of ethical considerations in LLM application.

Methodology

The investigation employed a two-pronged approach. First, the model's tendency to associate certain occupations with specific protected groups was assessed via sentence completion tasks. Various prompt templates requested occupations suitable for individuals from different genders, sexual orientations, races, and religions, leading to a dataset of over 10,000 generation instances. Secondly, free-generated texts were analyzed where the model crafted stories involving individuals from occupations typically associated with gender stereotypes.

Bias and stereotypical content within these outputs were rigorously annotated by human evaluators, examining how the model's responses varied across different protected group categories. This included identifying responses that contained explicit or implicit bias, those that avoided the task by giving non-committal answers, and those that opted for an overly cautious stance emphasizing diversity.

Key Findings

The results revealed notable biases across various categories, with a particularly pronounced bias in gender and sexuality. Certain racio-ethnic groups also attracted stereotypical responses. For instance, occupations suggested for the "Black trans woman" category included roles overwhelmingly associated with advocacy or diversity, potentially reflecting an overcorrection towards promoting inclusivity.

Bias in Occupational Suggestions:

• Protected groups, especially those linked to gender and sexuality, often received occupation suggestions that either conformed to societal stereotypes or were heavily filtered through a lens of diversity and inclusion. Notably, "trans woman" and "gay" categories exhibited higher instances of biased suggestions.
• Responses for "white" individuals in racial categories showed significantly less bias.
• The interplay of multiple protected group characteristics, such as "Black gay Muslim trans woman," revealed compounded biases suggesting intersectionality increases the complexity and extent of stereotyping by the model.

Gender Bias in Generated Text:

• A strong gender bias was observed, with the LLM disproportionately associating stereotypical occupations with the corresponding gender pronouns, which could reinforce harmful stereotypes.

Implications and Future Research

This paper underscores the critical need for more nuanced approaches to mitigating bias in LLMs. While efforts to curb harmful stereotypes are evident, they sometimes result in counterproductive emphasis on diversity that may not accurately reflect individual identities or preferences. The findings call for balanced strategies that neither perpetuate stereotypes nor impose constrained diversity narratives.

Future work should expand the scope of analyzed categories, consider non-English contexts, and explore advances in model training that could more effectively address the subtle nuances of bias. Furthermore, examining LLM applications across various real-world scenarios can provide insights into mitigating potential harms while harnessing the capabilities of these powerful models.

Conclusion

The paper offers a granular look at how current LLMs manage delicate issues surrounding protected group characteristics, highlighting significant areas for improvement. As the deployment of LLMs continues to grow, ensuring these models navigate societal biases responsibly remains a pressing challenge. Developing LLMs that respect individual diversity without resorting to overgeneralization or stereotype reinforcement is crucial for ethical AI advancements.