⚠️ Overfitting to Data with Temporal Correlations: A Pervasive AI Bias
A critical, yet often overlooked AI bias arises when training data exhibits temporal correlations, which are then inadvertently mimicked by the model. This phenomenon leads to poor predictive performance on unseen data, a phenomenon known as overfitting to temporal correlations.
What causes overfitting to temporal correlations?
When data is collected in a sequential manner, such as in time series or financial transactions, it often exhibits temporal correlations. These correlations can stem from various factors like seasonality, trends, or causality. However, if the model is not designed to capture these correlations correctly, it may overfit to them, leading to poor generalization.
Consequences of overfitting to temporal correlations
- Poor predictive performance: Models that overfit to temporal correlations tend to perform poorly on unseen data, as they have learned to fit the noise and pa...
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