recency bias

**Recency Bias** in neural network training is the **tendency for models to be disproportionately influenced by recently seen training examples** — especially in online or sequential training settings, the model's predictions are biased toward the data distribution of recent mini-batches, potentially forgetting earlier patterns. **Recency Bias Manifestations** - **Catastrophic Forgetting**: In continual learning, the model overwrites knowledge from earlier tasks with recent data. - **Order Sensitivity**: The order of training data affects the final model — later data has more influence. - **Streaming Data**: In online learning, the model tracks recent trends but may forget older patterns. - **Batch Composition**: The last few batches disproportionately affect predictions — temporal proximity matters. **Why It Matters** - **Data Ordering**: Shuffling training data mitigates recency bias — standard practice in SGD. - **Continual Learning**: Recency bias is the core challenge in continual learning — preventing it requires replay, regularization, or isolation. - **Process Monitoring**: Models deployed for drift detection must balance recency (adapting to new conditions) with memory (remembering rare events). **Recency Bias** is **the tyranny of the latest data** — the model's tendency to overweight recent examples at the expense of earlier knowledge.