Context Bias

Context bias, the undue influence of surrounding information on model predictions, is a significant challenge across various machine learning domains, particularly in tasks involving natural language processing and computer vision. Current research focuses on identifying and mitigating this bias through techniques like causal inference, counterfactual data augmentation, and careful dataset design, often employing model architectures that explicitly disentangle contextual and target features. Addressing context bias is crucial for improving the robustness, fairness, and generalizability of AI systems, leading to more reliable and accurate predictions in applications ranging from emotion recognition to entity typing and object detection.