Bias in natural language processing is a phenomenon where computational models reflect and perpetuate societal biases present in the linguistic data used for their training and evaluation. It represents a significant research area within computational linguistics, machine learning, and artificial intelligence. This concept investigates the sources, forms, and impacts of these biases, which can manifest as unfair or discriminatory outcomes across various NLP tasks, often reflecting harmful stereotypes related to demographics, social groups, or other protected attributes embedded within text corpora and model parameters. Key characteristics include the implicit capture of associations from large datasets, the potential for biased model outputs in downstream applications, and the technical challenges associated with detecting, measuring, and mitigating such biases. Its significance lies in the critical need to develop fair, equitable, and reliable NLP systems, addressing the profound socio-technical implications and preventing the perpetuation of discrimination or harm in real-world AI deployments.