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Earlier quoted context omitted.
I don't know about the legality, but with a good/recent language model it could be quite feasible. The problem is getting the good language model.
I assume you'd have to also give it a list of names that are possibly associated with the specific item.
The Mueller report got me thinking. How hard would it be to put together a machine learning tool that guessed at the redacted material based on: (a) The context of the surrounding words, and (b) In cases where just a couple words in a sentence are redacted, using the number of pixels to inform a likely combination of letters that would perfectly "fit" that space? And what would be the legality?
You can't do this because language models are not magic. Let's say you write a sentence like: "[your name] picked up the book." Censor it to take out your name and let a language model fill it in. It might give you "John picked up the book" or "Mary picked up the book", which are grammatically correct, but it has no way of guessing your name reliably because it has no information about the situation of the real world…
If you know where the comma ends and the next sentence begins, you still know the exact number of pixels in between. Let's then assume that there are no proper nouns in the missing text. There probably aren't very many combinations of letters that fit the space perfectly while still making for valid words, given different letters have different pixel widths.
But the idea wouldn't even be to come up with the CORRECT answer. It would be to assign a score to different options of what it could possibly say.
I agree you couldn't do paragraphs.