For those exercising their GDPR right to rectification, my general feeling is that a filter applied onto the model to prevent the invalid output should be consi

For those exercising their GDPR right to rectification, my general feeling is that a filter applied onto the model to prevent the invalid output should be considered sufficient here. Judging a system by an incomplete internal representation part way through the computation feels like a bar that essentially all systems would fail - like if someone's record initially has them as 0 years old for a few microseconds before fields are initialized, or if a data structure on disk is made up of a base checkpoint then sequentially-applied diffs. In a practical sense, for cases like the article's where the error seemingly arises out of random noise (due to relative obscurity of information about the subject) it's unclear to me to what extent that the false claim is really "in the model weights" in any way at all. In a potentially more philosophical sense, even for cases where it's not down to noise I'd question whether the weights/activations have real-world meaning beyond what they are presented to the user as. If some bool in a user's record is shown on the GUI as "Married" then that's the meaning of that bool. Equally if some activations are mapped to "Insufficient information about John Smith" instead of "John Smith is a murderer" then I feel that becomes the meaning of those activations and weights.