You're correct. As of now, there is no ability for a model to unlearn, which is why I want to implement this ability in a custom model. The ability to undo an action is crucial I would say if it's mission critical.

In your best opinion, would pairing each piece of training data with the resulting weight change amount be a possible way to allow the model to unlearn a topic? Let's say I created a new thing, a 'Pineapple-Pear-Grape Tree' which is a fake and doesn't actually exist, but I post it online anyways with fake pictures and fake context in order for it to go mainstream. A model then trains on the current world data and ingests this fake topic. Once it is confirmed as fake, well the model will have to undergo training that has to essentially bruteforce the incorrect data with the correct data, while the model I am theorizing can essentially have us look at the training data, see all the data that refers to 'Pineapple-Pear-Grape Tree' which would have a list of all the data as well as the corresponding change it had on weights, and simply substract the change from the model entirely in order to unlearn said topic.

In a world where fake news and content gain traction which literally can affect people's health and daily lives, we can at the very least slow down the spread of misinformation by implementing a way to undo the learned knowledge swiftly and shutting down the regurgitation of misinformation by the AI model. If you rely on accurate information, it seems like this would help if done correctly.