Language Bias

Language bias in artificial intelligence, particularly large language models (LLMs), refers to the systematic tendency of these systems to reflect and amplify existing societal biases present in their training data. Current research focuses on identifying and mitigating these biases across various domains, including sentiment analysis, hate speech detection, and visual question answering, often employing techniques like adversarial training, bias probing, and prompt engineering with transformer-based models like BERT and RoBERTa. Understanding and addressing language bias is crucial for ensuring fairness, equity, and trustworthiness in AI systems, impacting not only the scientific community's understanding of AI limitations but also the ethical development and deployment of AI technologies in real-world applications.