Emma A. M. Stanley

It is shown that predictions on a patient's unseen future data can change significantly if a model observed that same patient's anonymised historical data during training, a phenomenon known as memorisation bias, which exists across diverse data modalities and model architectures, and over prolonged time spans.

Attention pooling enhances performance but paradoxically amplifies subgroup disparities, challenging assumptions about fairness in model adaptation.