Language Fairness

Language fairness in artificial intelligence focuses on mitigating biases in natural language processing (NLP) models that disproportionately affect certain languages or linguistic groups. Current research emphasizes developing fairer multilingual models, often employing techniques like adaptive tokenization, multitask learning incorporating dialectal variations, and contrastive learning for debiasing. This work aims to improve the accuracy and equity of NLP systems across diverse languages, impacting applications ranging from machine translation and information retrieval to chatbot development and text classification, ultimately promoting inclusivity in AI.