A Delete Endpoint Proves Nothing
Removing rows from a training table does not establish that a released model forgot them. A useful audit must test residual membership signal and verify that retained behavior did not collapse.
unlearnaudit provides a deterministic k-nearest-neighbor harness with an exact-retraining reference, a membership-inference AUC measurement, and before-versus-after held-out utility.
Measure Privacy And Utility Together
One structured report captures forget and retain sample counts, leakage reduction, test accuracy delta, method, pass criteria, and scope. The CLI, API, workbench, and tests share that audit function.
The Demo
Membership AUC falls from 1.000 before deletion to 0.481 after exact retraining, a leakage reduction of 0.519. Held-out accuracy remains 0.988, so the audit passes with zero measured utility loss.
This small k-NN harness demonstrates the audit contract. It is not proof of deletion for an arbitrary model, optimizer, checkpoint lineage, or threat model.
Research Basis
The project is informed by OpenUnlearning, a NeurIPS 2025 benchmark that standardizes datasets, evaluation protocols, and privacy-utility assessment for machine unlearning.
Read the OpenUnlearning at NeurIPS 2025.