Common Bias

Common biases in machine learning models, particularly large language models (LLMs), are a significant area of research focusing on identifying and mitigating systematic errors in model outputs stemming from skewed training data. Current efforts concentrate on detecting biases related to geography, socioeconomics, demographics, and even seemingly innocuous factors like JPEG compression in image datasets, employing techniques like data augmentation, prompt engineering, and contrastive learning to improve model robustness. Understanding and addressing these biases is crucial for ensuring fairness, accuracy, and trustworthiness in AI systems across diverse applications, from news recommendation to human-robot interaction.