Nationality Bias

Nationality bias in large language models (LLMs) is a significant area of research focusing on identifying and mitigating unfair or stereotypical representations of different nationalities in AI-generated text. Current studies utilize various methods, including reinforcement learning techniques to debias models and bias probing methods to detect and quantify biases across multiple languages and model architectures like BERT and GPT. Understanding and addressing this bias is crucial for ensuring fairness and equity in AI applications, particularly those impacting hiring, job recommendations, and public perception.