Technology

I'm not an AI expert, and I wouldn't say it is too hard, but I believe removing a specific piece of data from a model is like trying to remove excess salt from a stew. You can add things to make the stew less salty but you can't really remove the salt.

The alternative, which is a lot of effort but boo-hoo for big tech, is to throw out the model and start over without the data in question. These companies would do well to start with models built on public or royalty free data and then add more risky data on top of that (so you only have to rebake starting from the "public" version).

It's actually a pretty normal thing in law. Laws are created with common sense in mind and compromises.

Currently EU laws do not cover generative AI. Now EU needs to decide how to deal with it. If consider it as a "lossy compressed database", trying to enforce a variation of gdpr with added fuzziness, or do something else

I just saw an article that said that ISPs are trying to whine their way out of listing the fees they charge because it's too hard. Which is wild because they certainly know what I owe them after I sign the contract, but somehow it's just impossible for them to determine right up until the moment that I'm obligated to pay it.

It's not just about having permission or not, but the right to be forgotten. You can ask a company to delete the personal data they may have on you and by law they should (in theory) delete it, with the only exception being data that may be required for justified purposes.

AIs not being able to "forget" means that they would be breaking the law if trained with personal data, as you could not have your data removed if you ask them to do so.

You're right, this is a way to solve this issue. It's just not economically feasible to retrain your model from scratch every time. It takes a lot of money to do it and they will push back.

Seems like exactly that

https://blog.research.google/2023/06/announcing-first-machine-unlearning.html?m=1

Model does not keep track of where it learns it from. Even if it did, it couldn't separate what it learnt and discard. Learning of AI resembles to improving your motor skills more than filling an excell sheet. You can discard any row from an Excell sheet. Can you forget, or even separate/distinguish/filter the motor skills you learnt during 4th grade art classes?

Think of it like this: you need a bunch of data points to determine the average of them all, but if you're only given the average of a group of numbers, you can't then go back and determine the original data points. It just doesn't work like that.

It’s closer to how you (as a person) know things than, say, how a database know things.

I still remember my childhood home phone number. You could ask me to forget it a million times I wouldn’t be able to. It’s useless information today. I just can’t stop remembering it.

It’s actually because they do know things in a way that’s analogous to how people know things.

Let’s say you wanted to forget that cats exist. You’d have to forget every cat meme you’ve ever seen, of course, but your entire knowledge of memes would also have to change. You’d have to forget that you knew how a huge part of the trend started with “i can haz cheeseburger.”

You’d have to forget that you owned a cat, which will change your entire memory of your life history about adopting the cat, getting home in time to feed it, and how it interacted with your other animals or family. Almost every aspect of your life is affected when you own an animal, and all of those would have to somehow be remembered in a no-cat context. Depending on how broadly we define “cat,” you might even need to radically change your understanding of African ecosystems, the history of sailing, evolutionary biology, and so on. Your understanding of mice and rats would have to change. Your understanding of dogs would have to change. Your memory of cartoons would have to change - can you even remember Jerry without Tom? Those are just off the top of my head at 8 in the morning. The ramifications would be huge.

Concepts are all interconnected, and that’s how this class of AI works. I’ve owned cars most of my life, so it’s a huge part of my personal memory and self-definition. They’re also ubiquitous in culture. Hundreds of thousands to millions of concepts relate to cats in some way, and each one of them would need to change, as would each concept that relates to those concepts. Pretty much everything is connected to everything else and as new data are added, they’re added in such a way that they relate to virtually everything that’s already there. Removing cats might not seem to change your knowledge of quarks, but there’s some very very small linkage between the two.

Smaller impact memories are also difficult. That guy with the weird mustache you saw during your vacation to Madrid ten years ago probably doesn’t have that much of a cascading effect, but because Esteban (you never knew his name) has such a tiny impact, it’s also very difficult to detect and remove. His removal won’t affect much of anything in terms of your memory or recall, but if you’re suddenly legally obligated to demonstrate you’ve successfully removed him from your memory, it will be tough.

Basically, the laws were written at a time when people were records in a database and each had their own row. Forgetting a person just meant deleting that row. That’s not the case with these systems.

The thing is that we don’t compel researchers to re-train their models on a data set if someone requests their removal. If you have traditional research on obesity, for instance, and you have a regression model that’s looking at various contributing factors, you do not have to start all over again if someone requests their data be deleted. It should mean that the person’s data are removed from your data set it it doesn’t mean that you can’t continue to use that model - at least it never has, to my knowledge. Your right to be forgotten doesn’t translate to you being allowed to invalidate the scientific models generated that glom together your data with that of tens of thousands of others. You can be left out of the next round of research on that dataset, but I have never heard of people being legally compelled to regenerate a model based on that.

There are absolutely novel legal questions that are going to be involved here, but I just wanted to clarify that it’s really not a simple answer from any perspective.

Not really, no. None of the source material is actually stored inside the model's dataset, so once it's in, it's in. Because of the way they are designed, you can't point to a particular document and just delete that one thing. It's like unscrambling an egg.

Yes, but that's not easy... I can't remember exactly, but I think I saw an estimate that the compute time to train just one of the GPT models cost around $66 million. IDK whether that's total cost from scratch, or incremental cost to arrive at that model starting from an earlier model that was already built, but I do know that GPT is still to this day using that September 2021 cutoff which to me kind of implies that they're building progressively on top of already-assembled models and datasets (which makes sense, because to start from scratch without needing to would be insane).

You could, technically, start from scratch and spend 2 more years and however many million dollars retraining a new model that doesn't have the private data you're trying to excise, but I think the point the article is making is that that's a pretty difficult approach and it seems right now like that's the only way.

You could certainly break up training data, but breaking up the models into mini models based on which training data is used wouldn't work with neural networks trained using gradient descent. Basically whatever the state of the model is it depends on the totality of the training data that it has been trained on (and the order) and it isn't possible to go and remove the effect of a specific training data point without then retraining for all of the data that followed that data point (and even that assumes you were storing a snapshot of the model before every single training data point, which I doubt anyone does)

However, that's no excuse and it is of course possible to entirely retrain a network using a clean dataset and that is what these companies should do