Temporal Bias

Temporal bias, the systematic error introduced by the time dimension in data or models, is a growing concern across various fields. Current research focuses on identifying and mitigating this bias in diverse applications, including large language models, climate modeling, and video retrieval, employing techniques such as bias correction methods (often incorporating machine learning, particularly attention models), and data augmentation strategies like shuffling video segments to reduce reliance on spurious temporal correlations. Addressing temporal bias is crucial for improving the reliability and generalizability of models, leading to more accurate predictions and fairer outcomes in applications ranging from epidemiological forecasting to abusive language detection.