This was a thing long before the AI craze: ieeexplore.ieee.org/document/9152669
AdGraph differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources… We evaluate AdGraph on the Alexa top-10K websites, and find that it is highly accurate, able to replicate the labels of human-generated filter lists with 95.33% accuracy, and can even identify many mistakes in filter lists.
They made a Chrome extension, in 2020: github.com/uiowa-irl/AdGraph
But practically, it’s tricky because:
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It still needs to load and render the ads. Not undoable; Adnauseum does this, but its a much more performance-intense approach.
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There’s always the risk of unpredictably blocking legitimate elements.
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Image/text classifiers aren’t actually black boxes. Hence there were also adversarial measures against this… before the AI craze: web.cs.ucdavis.edu/~zubair/…/a4-acsac2021.pdf
At the end of the day, the whole point of uBO is to be lightweight and fast. You aren’t wrong about a machine-learning based approach, but its not really a fit for them IMO.
But.
I think a reasonable approach would be to have bots and agents generate adblocking rules, to be tested by humans, then shipped to users of various adblocking engines.
The engineering is far easier. Performance/latency constraints are minimal, 1 bot can maintain thousands of rules for millions of users, and it isn’t necessarily availible to reverse engineer, either.
Some adblock lists must do this, already. I know they already use some automation, but I don’t know the details TBH.