Spatial Bias

Spatial bias in machine learning models refers to the uneven performance of these models across different spatial regions of input data, such as images or text representing geographical locations. Current research focuses on identifying and quantifying this bias in various applications, including object detection, image classification, and natural language processing, often using zone-based evaluation metrics to analyze performance across different spatial zones. Addressing spatial bias is crucial for improving the robustness and fairness of machine learning systems, ensuring reliable performance across diverse input data and mitigating potential biases in real-world applications. This research is driving the development of new algorithms and training strategies aimed at achieving more spatially balanced and equitable model performance.