Hierarchical Regional Bias

Hierarchical regional bias refers to the systematic errors in processing information that arise from the nested structure of geographical regions, influencing judgments about spatial relationships and impacting the performance of various models, including large language models (LLMs) and sequence-to-sequence models. Current research focuses on identifying and quantifying this bias using novel evaluation metrics and exploring how different model architectures, such as transformers and graph-based methods, handle hierarchical information during training and inference. Understanding and mitigating this bias is crucial for improving the fairness and accuracy of AI systems across diverse applications, particularly those involving geographical data or requiring nuanced spatial reasoning.