If you look at a hundred paintings of faces and then make your own painting of a face, you’re not expected to pay all the artists that you used to get an understanding of what a face looks like.
That’s because I’m a human being. I’m acting on my own volition and while I’ve observed artwork, I’ve also had decades of life experience observing faces in reality. Also importantly, my ability to produce artwork (and thus my potential to impact the market) is limited and I’m not owned or beholden to any company.
“AI” “art” is different in every way. It is being fed a massive dataset of copyrighted artwork, and has no experiences or observations of its own. It is property, not a fee or independent being. And also, it can churn out a massive amount of content based on its data in no time at all, posing a significant challenge to markets and the livelihood of human creative workers.
All of these are factors in determining whether it’s fair to use someone else’s copyrighted material, which is why it’s fine for a human being to listen to a song and play it from memory, but it’s not fine for a tape recorder to do the same (bootlegging).
Btw, I don’t think this is a fair use question, it’s really a question of whether the generated images are derivatives of the training data.
I’m not sure what you mean by this. Whether something is derivative or not is one of the key questions used to determine whether the free use of someone else’s copyrighted work is fair, as in fair use.
AI training is using people’s copyrighted work, and doing so almost exclusively without knowledge, consent, license or permission, and so that’s absolutely a question of fair use. They either need to pay for the rights to use people’s copyright work OR they need to prove that their use of that work is “fair” under existing laws. (Or we need to change/update/overhaul the copyright system.)
Even if AI companies were to pay the artists and had billions of dollars to do it, each individual artist would receive a tiny amount, because these datasets are so large.
The amount that artists would be paid would be determined by negotiation between the artist (the rights holder) and the entity using their work. AI companies certainly don’t get to unilaterally decided what people’s art licenses are worth, and different artists would be worth different amounts in the end. There would end up being some kind of licensing contract, which artists would have to agree to.
Take Spotify for example, artists don’t get paid a lot per stream and it’s arguably not the best deal, but they (or their label) are still agreeing to the terms because they believe it’s worth it to be on those platforms. That’s not a question of fair use, because there is an explicit licensing agreement being made by both parties. The biggest artists like Taylor Swift negotiate better deals because they make or break the platform.
So back to AI, if all that sounds prohibitively expensive, legally fraught, and generally unsustainable, then that’s because it probably is–another huge tech VC bubble just waiting to burst.
Whether something is derivative or not is one of the key questions used to determine whether the free use of someone else’s copyrighted work is fair, as in fair use.
I think training an AI model is not fair use. It’s either derivative work and needs a license or it’s not derivative work and can be used without a license. In both cases it’s not fair use (in the legal sense of “fair use”).
I’m not sure if you’re making an argument about what the law currently says or what it should say. In my opinion the law should be updated to clarify if you need a license to use copyrighted material as training data.
The amount that artists would be paid would be determined by negotiation between the artist (the rights holder) and the entity using their work
Sure, my point is such an agreement will never be made. It’s a good deal for AI companies to use the data for free, but if they can’t do that, they will not be interested.
Either way, I think there is no way for artists to win this. It’s completely possible to train large image generators without copyrighted material. These datasets are so large that paying artists per image will never be feasible.
I mean ML is just the tool someone used to study a work in a way that generated a new understanding (the model). You could theoretically accomplish the same thing with a mathematician measuring painting with a variety of instruments and building a mathical model themselves to represent the relationship between word descriptions and the images produced.
The work of thousands of devs, engineers and scientists have just made that process both more wildly available and applicable.
That’s because I’m a human being. I’m acting on my own volition and while I’ve observed artwork, I’ve also had decades of life experience observing faces in reality. Also importantly, my ability to produce artwork (and thus my potential to impact the market) is limited and I’m not owned or beholden to any company.
“AI” “art” is different in every way. It is being fed a massive dataset of copyrighted artwork, and has no experiences or observations of its own. It is property, not a fee or independent being. And also, it can churn out a massive amount of content based on its data in no time at all, posing a significant challenge to markets and the livelihood of human creative workers.
All of these are factors in determining whether it’s fair to use someone else’s copyrighted material, which is why it’s fine for a human being to listen to a song and play it from memory, but it’s not fine for a tape recorder to do the same (bootlegging).
I’m not sure what you mean by this. Whether something is derivative or not is one of the key questions used to determine whether the free use of someone else’s copyrighted work is fair, as in fair use.
AI training is using people’s copyrighted work, and doing so almost exclusively without knowledge, consent, license or permission, and so that’s absolutely a question of fair use. They either need to pay for the rights to use people’s copyright work OR they need to prove that their use of that work is “fair” under existing laws. (Or we need to change/update/overhaul the copyright system.)
The amount that artists would be paid would be determined by negotiation between the artist (the rights holder) and the entity using their work. AI companies certainly don’t get to unilaterally decided what people’s art licenses are worth, and different artists would be worth different amounts in the end. There would end up being some kind of licensing contract, which artists would have to agree to.
Take Spotify for example, artists don’t get paid a lot per stream and it’s arguably not the best deal, but they (or their label) are still agreeing to the terms because they believe it’s worth it to be on those platforms. That’s not a question of fair use, because there is an explicit licensing agreement being made by both parties. The biggest artists like Taylor Swift negotiate better deals because they make or break the platform.
So back to AI, if all that sounds prohibitively expensive, legally fraught, and generally unsustainable, then that’s because it probably is–another huge tech VC bubble just waiting to burst.
I think training an AI model is not fair use. It’s either derivative work and needs a license or it’s not derivative work and can be used without a license. In both cases it’s not fair use (in the legal sense of “fair use”).
I’m not sure if you’re making an argument about what the law currently says or what it should say. In my opinion the law should be updated to clarify if you need a license to use copyrighted material as training data.
Sure, my point is such an agreement will never be made. It’s a good deal for AI companies to use the data for free, but if they can’t do that, they will not be interested.
Either way, I think there is no way for artists to win this. It’s completely possible to train large image generators without copyrighted material. These datasets are so large that paying artists per image will never be feasible.
I mean ML is just the tool someone used to study a work in a way that generated a new understanding (the model). You could theoretically accomplish the same thing with a mathematician measuring painting with a variety of instruments and building a mathical model themselves to represent the relationship between word descriptions and the images produced.
The work of thousands of devs, engineers and scientists have just made that process both more wildly available and applicable.