Mitigating Bias in Large Language Models Associated with Occupational Stereotypes

Abstract

The rise of Large Language Models (LLMs) has revolutionized text generation yet concerns persist about their potential to reinforce societal biases. Previous research has demonstrated that AI systems can amplify existing gender stereotypes through biased training data and algorithmic design. This study critically examines potential gender biases in four widely used large language models by analyzing their responses associated with various prompts related to professional occupations. Our findings reveal systematic gender disparities in AI-generated content, often reflecting and at times intensifying existing occupational stereotypes. For instance, male executives were overrepresented in AI-generated lists, whereas caregiving roles such as nursing were predominantly associated with women. In some cases, these biases deviated from real-world gender distributions, suggesting that AI systems may introduce distortions beyond societal norms. In this paper, we also discuss two strategies to mitigate gender disparities and show that these strategies can significantly improve fairness and inclusivity in AI-generated representations of professional roles.

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