NLP models can reinforce biases present in their training data, leading to outputs that reflect societal stereotypes or prejudices. For example, if a training dataset disproportionately associates certain professions with specific genders, the model might produce biased predictions or completions. Similarly, word embeddings like Word2Vec have demonstrated biases by associating "man" with "doctor" and "woman" with "nurse."
Bias reinforcement occurs during the data collection and preprocessing stages, as datasets often reflect historical inequities or cultural stereotypes. Models trained on biased datasets inherit these patterns, which can then perpetuate discrimination in real-world applications, such as hiring systems or predictive policing.
Addressing bias requires careful dataset curation, debiasing techniques in embeddings, and fairness-aware algorithms. Regular audits and evaluations of model outputs are also essential to identify and mitigate biases. Tools like AI Fairness 360 and interpretability methods (e.g., attention visualization) help developers identify and reduce bias in NLP systems.