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Comment by rubinelli | Hacker News Reader

De minimis is a longstanding defense in copyright law. If you are copying very little from very many works, as is the case when you turn multiple petabytes into a few gigabytes of neural network weights, you are in the clear. The problem arises when models overfit and spit out almost perfect copies of the training data.

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For example, I could take a massive 8k video and covert it into a very small 144p youtube video. Am I in the clear simply because the output is tiny compared to the input? Similar I could take a huge studio master copy of a song and convert it to a very small and rather compressed (distorted) mp3.

I partially agree that some of the problem is when perfect copies are spit out by the models, but I do think there is a bigger problem. Copyright is a complex concept that can't be defined exclusively by a single metric like size, and any mathematically definition will in the end be killed if large copyright holders feel threatened by it.

Thumbnail images don't violate copyright, and are a very helpful comparison case to consider.

"Transformative Use" is a major consideration in fair use copyright: https://en.wikipedia.org/wiki/Transformative_use

ML models do not supplant the pre-existing work, and provide fundamentally new modalities. Transformative use seems like a slam dunk to me, but I guess we'll see what the Supremes decide in twenty years or so...

So is market harm

"Some courts have held this factor to be the most important in the analysis."

https://ilt.eff.org/Copyright__Fair_Use.html#Market_Harm

I’m unclear about this. Let’s say a movie comes out and I make a YouTube review using brief clips or screenshots from the movie. Since my review is transformative, I should be in the clear (I think?).

But when it comes to market harm, does the tone of my review effect the enforceability of copyright?

As in, if my review is negative it would harm the market for people going to watch the movie vs a positive review right?

Reviews have a distinct "character of use", one of the four cornerstones of fair use exceptions.

A review can be commercial, can cause significant harm to the market, can include substantial amount of the work, and yet the character of use can be significant enough to convince a judge that a exemption should be applied. Since judges historically has come to this conclusion there exist now legal precedence. With precedence we can make some general conclusions which tell us that reviews are in general exempted when using other peoples copyrighted work for the purpose of reviews.

This character of use is very different then if I convert a studio record of a song into mp3 and publish it on p2p sharing site. Judges has historically viewed the character of use in those situation as not being worth giving exemptions.

I am not a lawyer but I'd think:

You're not directly competing with the movie though, your work is a review, not a feature film.

If you were to make a parody movie from the material of the movie itself, directly taking scenes and altering them to your liking but still relying on the viewer recognizing the original in it, you'd have a harder time, I think.

There's a Stable Diffusion example where, having been trained on too many Getty Images pictures stamped with their logo, the system generated new images with Getty Images logos.[1] That's a bit embarrassing. There are code generation examples where copyright notices appeared in the output. A plagiarism detection system to insure that the output is sufficiently different from any single training input ought to be possible.

[1] https://petapixel.com/2023/01/17/getty-images-is-suing-ai-im...

Yes, agreed, I don't think the problem is with networks that mix tons of input data in a way that doesn't heavily derive from one or a couple of sources. The currently available models do not have overfitting solved, though, and this technological imperfection also has direct practical (and legal) consequences.

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