Determine the downstream utility of low-fidelity reproductions classified as forgetting

Determine whether low-fidelity reproductions of training features that fall below the classifier detection threshold are useful for downstream clinical tasks or instead degrade performance similarly to complete omission.

Background

The proposed auditing framework may classify some generated samples as forgotten when their reproduced features are too weak for the identity or fingerprint classifiers to detect. Such samples may not represent true omission; they may instead be low-quality generalizations. The clinical usefulness of these low-fidelity outputs, and their effect on downstream performance, therefore requires task-specific evaluation.

References

Whether such low-fidelity reproductions are useful for downstream clinical tasks, or whether they degrade performance similarly to complete omission, remains an open question that requires task-specific evaluation.

— A Data-Interventional Framework for Auditing Privacy and Fairness in Generative Medical Imaging  (2609.26623 - Dombrowski et al., 22 Sep 2026) in Section 6, Limitations