Bias Annotation

Bias annotation focuses on identifying and mitigating biases present in datasets used to train machine learning models, primarily aiming to improve model fairness, robustness, and generalization. Current research emphasizes developing methods to detect and correct for these biases, employing techniques like multi-task learning with pre-trained models (e.g., using RoBERTa), and leveraging neural network architectures to disentangle bias from true signal. This work is crucial for advancing the reliability and ethical implications of AI systems across diverse applications, from medical image analysis to media bias detection and scene graph generation.