Machine Unlearning: The Right to Be Forgotten

GDPR's "right to erasure" is easy to grant for a database row and hard to grant for a trained model — a naive fix means retraining from scratch every time someone asks to be forgotten. This simulator builds a real SISA-sharded ensemble of logistic-regression classifiers over a synthetic 2D dataset, each shard trained by genuine gradient descent on cross-entropy loss, and renders the ensemble's live decision surface as a colored 3D landscape. Click any training point to file an erasure request, then process it and watch only that point's shard retrain while every other shard — and its portion of the model — stays untouched, with a running counter comparing the true SISA retraining cost against what a naive full retrain would have cost.