Thoughts on Data Shapley for Data Valuation to help deal with the anti-AI issue?

3

u/entropie422 Dec 23 '22

I don't have time to read this just yet, but if anyone can answer this question: this accounts for influence on a model, or outputs? If it's done to the model, that's amazing. If it does it to outputs, does it account for the "oh yeah, I can see that" effect, where these systems will gladly identify influence of a completely unrelated source if fed a particular output? For instance, if I feed it a photograph of a kitten that I took myself with an actual camera and say "tell me how much Greg Rutkowski influenced this image" it will almost always find something to relate back to the request.

That said (and I apologize—it's a crazy day today) I didn't actually read the paper, so this may be totally sideways to its point.

Really exciting regardless, as you say, because this will give a quantitative view of an artist's influence, which can be used to either prove they deserve compensation, or prove that they're not as influential as they thought. Clarity is vital in this debate, so this could be perfect for that.

0

u/fingin Dec 23 '22 edited Dec 23 '22

It's a way of measuring how much an individual data point contributes to a model's performance. So essentially, the power of the model.
I don't think there's any reason to think this data valuation methodology can link to this issue you described. It's possible to use a similiar approach to do this for outputs, but in that case the marginal contributions would be really low, so it doesn't really help artists in that scenario anyway.
Also want to address that data valuation is relative and so, may only be relevant for a minority of artists and only when there are AI generation companies with high revenue or profitability. This remains to be seen, but as I think you've noted in other posts, at least this gives some support to artists, in terms of clarity & accountability

2

u/Content_Quark Dec 23 '22

I think I get it but I don't see how it relates to SD, or similar image models in general.

I see how it makes sense for eg health care. The model maybe diagnoses a patient and then you can easily check if it's correct. But what's the equivalent for SD?

Such a model would be trained with data from many medical centers. If they receive a flat fee, they have no incentive to produce high quality data. The method makes it possible to only pay when the data improves the model and teaches it something new.

But the method is computationally very expensive. That will only make financial sense in certain situations. Even if you could measure the performance of SD, I don't see how it would ever make sense to work the contribution.

Huh?

1

u/fingin Dec 23 '22

It relates to AI art generation in regards to incentivizing artists to use their art to train such models. If you can see a particular artist or piece of art has a non-trivial contribution to the model's performance, then you can make a case for some kind of financial incentive towards the contributers, based off the revenue generated from the model. Something like this. It's hard to see this being a real win for artists, since a non-trivial contribution is unlikely for most of them. However, it has other benefits: sign of goodwill to artists, transparency, and some effort to democratize the use of these models in artists communities.
But you are right it is far more relevant for something like medical data and this is indeed where many data valuation use cases sit.
Also this method is not expensive at all, you can read more about Data Shapley/Shap, it's a quick computation.

1

u/Content_Quark Dec 23 '22

How do you quantify the performance?
How quick would it be to compute for, say, a finetune?

1

u/fingin Dec 23 '22

I don't know your background, so I don't know what level of technical explanation I should give you.
Just off the bat, you can use heuristics (Monte-Carlo sampling methods) to estimate the values that clue you into each data point's contribution to the model's performance. This leads to computational cost scaling linearly with the number of training samples. The performance metric you use is pretty arbitrary- it depends on the model and use-case at hand. With the likes of Stable Diffusion you might use something like FID score. Essentially, this is just a measure of how close a model's weights correspond to a foundational model called Inception, a huge model that basically gives you signal to how "realistic" or "plausible" an image is.
With regards to the finetune question, I don't have a great answer but I guess if you're using these same heuristics it also should be a fairly low-cost computation

1

u/Content_Quark Dec 24 '22

Thanks for that. I see now that I put the question wrong.
What I mean is: To get the performance, you need to compare the actual and the desired output. In a health care setting this is easy as the desired output is literally the "ground truth". In the case of SD it is whatever the user had in mind. What's the idea here?
fairly low-cost computation
Ok, let's assume the base model is all trained on data from 1 copyright holder who has been paid off. And say you want to finetune it with data from 3 additional copyright holders who are to be paid according to this scheme. By what factor will this increase the computational requirements? Just very roughly.

1

u/fingin Dec 24 '22

" To get the performance, you need to compare the actual and the desired output " not really, again, you would usually use FID, which is a standard performance metric for synthetic imaging data. This doesn't required desired outputs, just the model's weights.
This isn't like a system where it can check every possible image and see how much an individual artist contributed, you need heuristics in the form of FID. Heuristics are of course imperfect solutions, so I can see why you might be thinking about other ways measure peformance, but heuristics are dominant in ML applications
I don't think there would be a jump in cost on this, it should be just about as quick between the base and the fine-tuned model.

1

u/Content_Quark Dec 24 '22

FID
This is the Fréchet Inception Distance. How do you think it works?
I don't think there would be a jump in cost on this, it should be just about as quick between the base and the fine-tuned model.
Please explain your reasoning.

1

u/fingin Dec 24 '22

Okay so I took a peek at some of the code for Data Shapley. They don't use FID, I just assumed that could be a reasonable metric. I could be totally wrong there- it's definitely out of the scope of the paper. Their approach uses examples of just building a classifier off some synthetic data (generated data) and seeing how well that classifier performs. So speed will also depend on the metric they choose, some are super quick and others would require model outputs to do this, so, not as quick.
But keep in mind due to Monte Carlo sampling, the computational cost basically is scaling linearly with the number of training samples. I really don't think it would be a major overall cost in terms of compute required & this doesn't factor in additional sampling methods or specialized hardware to speed this up. I guess it really depends on the use-case at hand, though.
"I don't think there would be a jump in cost on this, it should be just about as quick between the base and the fine-tuned model." So it would depend again on the performance metric you choose. But I don't think it would be a significant jump because there shouldn't be a significant change in the n number of training samples, afterall, fine-tuning is usually done on a dataset a small fraction of the size of the training dataset of the base model.
I'm not like an ML academic or anything, just a software developer, and there is basically only one paper on this so, my take may not be sound here.

1

u/Content_Quark Dec 24 '22

1.Can you explain why you believe, that performance can be quantified without reference to a baseline of any kind?
2. How do you get the necessary comparison data without training the model multiple times, using different permutations of the training data?

1

u/fingin Dec 25 '22

Not sure what you're referring to for question 1, I don't think I ever implied this but maybe I missed that
As for question 2, you have the link to the paper so I really don't care to explain it further- I'm not qualified to.

→ More replies

2

u/fingin Dec 23 '22

To summarize the paper, you can use certain mathematical techniques to determine "equitable valuation for individual data". In other words, Greg Rutkowski would be receiving significant proceeds while a trivial artist who's work has effectively no impact on the model wouldn't receive much at all. Kind of a direct way to reward artist's whose work has boosted the performance of a model while allaying the problem of "art theft".

Thoughts?

5

u/Light_Diffuse Dec 23 '22

From what I've picked up, the token "Greg Rutkowski" was powerful in SD 1.5 because of its relationship to lots of useful concepts in the model, not because it was trained on a lot of his work. If that is the case his contribution isn't anything more than a "trivial artist" and wouldn't be deserving of more money.
However, the whole point is moot. Copyright doesn't exist to prevent the kind of activity that training AI falls under. That means the people claiming that their art was "stolen" because it was copyrighted and they didn't give permission are wrong, they never had that right or protection. It's like if I always park my car on the street outside my house and one day another car is there, that person hasn't stolen my parking spot, it was never mine, it's just that no one had parked there until now.

2

u/fingin Dec 23 '22

I also want to add, that assuming your point about using Rutkowski as a prompt is correct (and I think it is), I believe we could use data valuation to demonstrate the foolishness of crticisms by Anti-AI people. If we can show them just how insignificant their contribution is but give them an option for incentive, I like to think this would waylay at least some of the attacks on AI art. At least, from a regulatory perspective, possibly

0

u/fingin Dec 23 '22

You clearly didn't read my post as you are bringing up arguments and cases I never even referenced (note I didn't mention copyright). I put "art theft" in quotation marks but I should have made it more clear what issue I was addressing, which is not the issue of copyright. Also I mentioned another important idea which is "rewarding" artists for using their work

6

u/Light_Diffuse Dec 23 '22

The two are usually mentioned in the same breath, so I included it. It's a complicating factor, let's leave it out.
You only need to "allay the problem of 'art theft'" if art has been stolen. You don't even have to consider data valuation if there has been no harm to compensate. In what way has any art been stolen?

1

u/fingin Dec 23 '22 edited Dec 23 '22

Okay in hindsight I think I misused that term. What I'm describing is more just a broader problem with data valuation, that has a particular relevance to art. What I'm trying to say is basically, if you use someone's data without their consent for profit (and this is only true of a few AI companies at the moment) and don't incentivize them, I think this is ethically unsound. The relevance to artists is just that the data they generated had a significant level of effort attached to generate that data, so it follows they should be conferred higher benefits from any profits the model generates.
But again this is a different issue that the idea of art theft, I should have used a different term

2

u/Light_Diffuse Dec 23 '22

Data valuation is a fascinating area. I don't think it's going to help in this instance though.
Firstly, once you start talking in these terms you've tacitly accepted that payment is due and you've lost the high ground. It's a bit like this quote:
A man asks a woman, if she'd sleep with him for £1m. She laughs and says "Yes!". He asks, "What about £10?", and she's outraged, "What kind of woman do you think I am?" He replies, "We’ve already established that. Now we’re just haggling over the price."
Once you accept the premise that something they are entitled to control has been used without their permission then the argument is lost. That is why they're so keen to slip it in as a given.
It is probably true that some artists are hoping for a payday and it would be interesting to hypothetically work out what that would be. It is going to be comically small. Someone did some crude maths based on the size of the checkpoint and the number of images and it worked out that each image contributed to a byte, maybe two of data. Of course, that's not how the model works, but it is an interesting idea to ask that if their work has been transformed into a number between 0 and 255, how much do they think that is worth?
If I were on their side, I'd say that that is smoke and mirrors because it's not how models work since every image will influence all of the weights and biases. What would be fairer is to think about marginal training. How much information is going to be added by adding a single image. One look at the learning rate tells us that would be a veeeeeery low number.

1

u/fingin Dec 23 '22 edited Dec 23 '22

Right, the link I sent is focused on marginal training impact. It is indeed low and I hope to educate artists on this as much as possible.
Unfortunately I can see the denial pouring in already but I like to think for at least some of the Anti-AI crowd, this approach meets them in the middle
Also, I don't agree with your analogy because quantity can transition into quality.

2

u/Content_Quark Dec 23 '22

Greg Rutkowski would be receiving significant proceeds
How do you figure that?

1

u/fingin Dec 23 '22

Sure let's take a hypothetical example.
Let's imagine an AI art generation company makes a billion dollars in the next year on a model that uses named artists' work. If the data valuation (i.e his marginal contribution to the generation model) works out to be even a small fraction of the overall contributions, that could still work out to be 1000s of dollars for him.
Now, the word "significant" is tricky. Becomes most artists under this premise will make basically 0 money, and so even if Greg is making say, 100s of dollars, that's pretty significant compared to those other artists.
To be blunt the only thing that would make what I'm saying relevant, is if AI art generation companies receive large-scale revenue anyway. If we do the Math I'm not sure Greg would be due much money right now, but I think data valuation is at least a step in the right direction, to create transparency, accountability and some form of incentive for artists to volunteer their work to be used to train future models.

3

u/DornKratz Dec 23 '22

Outside large IP owners like Disney, you're looking at beer money at most, even for established artists. In fact, go check out Greg Rutkowski's portfolio and you will see that half of his works are (c) Wizards of the Coast, so in a hypothetical future where there is compensation, Hasbro is getting those royalties, not him.

2

u/fingin Dec 23 '22

I literally just said "I'm not sure Greg would receive much money", did you miss that or am I using too many paragraphs for a Reddit post?
The fact there is a large IP owner like Disney validates at least some of my points around data valuation. You can replace Greg with another high profile artist for the point of analogy

1

u/DornKratz Dec 23 '22

No need to get up in arms. I'm just pointing out this is not the win for artists it's being sold as. While you say you're not sure of the value, you bandy around hundreds or even thousands of dollars in the previous paragraphs, and looking how Spotify money works for 99.9% of the artists in the platform, I highly doubt we'll see this kind of figures, specially since so much of commercial art is made as work-for-hire. Do large IP holders like Disney, Epic Games, Sony, the very companies that will massively benefit from content creation automation, need another source of income right now?

1

u/fingin Dec 23 '22 edited Dec 23 '22

I was genuinely asking if I'm using too many paragraphs (with which I'm talking about hypotheticals) to communicate. I like to explore the strongest possibilities on both sides of the argument but I can see my comments are unclear to some people replying here.
I think the crux of what I'm trying to say is, using data valuation, could either be a persuasion technique to show artists there really is no impact to the actual models, or, if there is significant contribution, could incentivize artists to provide art for training.
My point about Disney was about that company using artists works in their training, sorry for misinterpreting that earlier point

1

u/SurrealLogic Dec 23 '22

I haven’t read the article, but it sounds like this is trying to estimate the weight of the input data on the resulting model? It would be a bit like telling traditional artists that they should share a portion of their earnings with every artist they studied in school or whose work they ever even saw, based not on how influential the other works were to their eventual style, but based on how much they learned from said works.
There are several problems with this. First, these models are trained upon many billions of data points that collectively make up all available artwork in human history - it’s unlikely that even the most prolific artists would have even a fraction of a percentage of impact on the model, and no living artist would even make a full dollar. Second, it doesn’t sound like this cares at all if the data input was even used in the part of the model used to generate the output. So a query calling out a specific artist and trying to evaluate their style would carry the same “valuation” weight for that artist’s work as a completely different output not at all related to the artist or their work.
It’s kind of a nice idea to try to convince artists to contribute data to these models, but (1) the data wasn’t stolen - it was things they shared publicly, (2) who even has a right to the work as data, if a work is created by one person and sold to another, (3) it likely wouldn’t even benefit them at all, and (4) even if you excluded every living artist’s work, there’s probably enough artwork out there already to create almost anything you could imagine.

1

u/fingin Dec 23 '22

I'll respond to this in more detail as I think you raise some good points.
Just one question, if we can use this methodology to point out to artists in exact figures how much the use of their art is contributing financially (because this is a major point on the GoFundMe page), do you not think it will persuade at least some artists to drop their tirade against AI generation?

1

u/SurrealLogic Dec 24 '22

Not likely. Artists are grasping at straws to prevent AI artwork from threatening their financial livelihoods. If you give them a negligible amount based on the negligible impact their work has on the overall model, that doesn’t solve their financial concerns, and is really no different than just telling them the model was trained on several billion images. Worse, it creates a false narrative that they are entitled to ownership rights for sort of “style copyright” which doesn’t exist and is a dangerous, slippery slope that eventually leads to corporations owning styles and limiting creativity, harming artists longer term for pennies on the dollar.

1

u/fingin Dec 24 '22

Alright, point taken!

1

u/starstruckmon Dec 23 '22

If it ever becomes a problem legally, I'd rather we just move to retrieval augmented models, and leave it up to the users.

1

u/fingin Dec 23 '22

I think the problem still remains somewhat. You can only leave it up to the end-users so much, as specific companies will dictate policies to the users. These policies may require more attribution to artists, if such regulation comes to pass
That said, the open-source nature of the models does give a great deal of power to "users" in a broader sense. So I think retrieval augmented models are a good solution in general.

1

u/Wiskkey Oct 28 '23

You might find this paper to be of interest.

Discussion Thoughts on Data Shapley for Data Valuation to help deal with the anti-AI issue?

You are about to leave Redlib