GENDER BIAS IN LARGE LANGUAGE MODELS AND WORD EMBEDDINGS - Komilova Nilufar Abdilkadimovna (International Journal of Literature and Language Studies, 2026)
Abstract. This article examines the presence and mechanisms of gender bias in natural language processing (NLP) systems, particularly large language models (LLMs) and word embeddings. It explores how AI systems infer gender from textual data, the ethical implications of such inference, and the ways in which stereotypes are encoded in training datasets. The study synthesizes findings from computational linguistics, machine learning fairness research, and sociolinguistics to explain how bias emerges in data-driven language technologies. It also discusses current approaches for detecting gender stereotypes in datasets and evaluates mitigation strategies aimed at improving fairness in NLP systems.