Exploring Bias in NLP Models: Analyzing the Impact of Training Data on Fairness and Equity

Abstract

Natural Language Processing (NLP) technologies have revolutionized human-computer interactions, allowing machines to understand and generate human language with unparalleled precision. This advancement has created numerous applications, from virtual assistants and chatbots to sentiment analysis and automated content generation. As more models become incorporated into systems affecting people's lives--for instance hiring algorithms, judicial decision-making tools, or social media content moderation--they raise serious concerns over bias and fairness. Examining the factors contributing to bias within NLP models is of utmost importance, specifically the influence of training data on their performance. Training data selection and curation have an enormous influence on a model's ability to perform equally across diverse demographic groups; biased selection may reinforce existing stereotypes while poor representation may lead to underperformance for marginalized communities. Preprocessing techniques such as tokenization and normalization may inadvertently perpetuate biases if not applied with care. Through an in-depth literature review and case studies, this paper explores the sources of bias within NLP systems. Furthermore, various mitigation strategies for mitigating such biases to promote fairness within these applications are proposed in order to increase equity. [1] By identifying best practices for data curation, employing fairness-aware algorithms, and setting robust evaluation metrics, our aim is to develop NLP technologies that are not only effective but also just and equitable. The findings highlight the significance of responsible AI practices while encouraging developers and researchers alike to prioritize fairness as an essential aspect of NLP system design.