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> Fair use is a doctrine in the law of the United States that allows limited use of copyrighted material without requiring permission from the rights holders. I

> Fair use is a doctrine in the law of the United States that allows limited use of copyrighted material without requiring permission from the rights holders. It provides for the legal, non-licensed citation or incorporation of copyrighted material in another author's work under a four-factor balancing test: > 1) The purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes > 2) The nature of the copyrighted work > 3) The amount and substantiality of the portion used in relation to the copyrighted work as a whole > 4) The effect of the use upon the potential market for or value of the copyrighted work [emphasis from TFA] HN always talks about derivative work and transformativeness, but never about these. The fourth one especially seems clear in its implications for models. Regardless, it makes it seem much less clear cut than people here often say.

flangola7 3y ago
Microsoft is gambling on the hope that model training will be ruled fair use. This makes it seem that outcome is unlikely.
- brookst 3y ago
  Do you think a human learning something from reading is fair use? Or are we all copyright violators because reading that article altered our connectomes, and we may recall parts of it later?
  - snickerbockers 3y ago
    Yes it is considered fair use but it's also completely irrelevant because we're talking about a computer program not a person.
  - ethanbond 3y ago
    The point being raised is quite specific. Not sure if you’re willingly ignoring it or what? The answer is no, because you reading the article didn’t dramatically degrade its market value. An AI ingesting all content on the internet and then being ultra-effective at frontrunning that content for a large number of future readers does degrade its market value (and subsumes it into the model’s value).
    - cma 3y ago
      > The answer is no, because you reading the article didn’t dramatically degrade its market value. How about if you read a news article to write a competing one rewording and possibly citing it (one of the most common practices in news)?
      - ethanbond 3y ago
        How about it? Do you not think it incurs a lot of negative effects?
        deleted 3y ago
        [deleted]
        cma 3y ago
        What's the alternative, each news event is first to publish exclusivity regardless of quality? No synthesizing multiple stories into a linked narrative?
    - everforward 3y ago
      This applies to so many things, though. The most obvious parallel to me is YouTube. There are a ton of people ingesting books, then transforming that information into a roughly paraphrased video for people to watch for free (ish). That devalues the books they read and paraphrased, because other people don't need to read them. Spark Notes devalue actual books in a way, because a lot of high schoolers read those instead of buying the actual book. Search engines have also supplanted books in large part, because I don't need a whole book to answer a specific question. I don't know anyone that owns an encyclopedia anymore. This is the next iteration of these processes. Non-novel information's market value has been degrading for decades now. A series of questions that would have cost thousands of dollars in books to answer in the 70's/80's is now free, with or without AI.
      - llamaimperative 3y ago
        LLMs are attracting so much positive attention because they are likely to be a huge, huge step change improvement than all those methods you mention. For that same exact reason, it’s totally reasonable they’re attracting unique amounts of negative attention too. You can’t have it both ways: yes LLMs are going to change information retrieval the way nothing else has before, but no it’s actually just like all the other things in terms of their impact on incentive structures. FWIW I don’t really know where I land on this issue. I just find it totally incoherent to believe in the bull case of “this will transform everything” while also portraying it all as par for the course when discussing potential negatives. Just because Spark Notes didn’t obviously manage to kill valuable parts of our information ecosystem and economy does not mean that Spark Notes x 10,000,000 will not.
civilitty 3y ago
That’s not at all clear to me. IANAL but first of all it’s a balancing test, not a bright-line test. The judge could focus on any one factor and make an argument for either side quite easily. Second, “use” here could mean one of two things: training or inference. It’s publishing the results of inference that can lead to actual effects on the market, not the training. At the end of the day, someone has to prove tangible harm.
amluto 3y ago
I would look at #1 here. Crawling the Internet to collect information is one thing. (And people putting text on the web without requiring authentication seem to be granting at least some kind of license to anyone who sends a GET request.). But crawling the Internet (via centralized robots or users’ browsers), then storing that data and charging money to others for rights to that data (as Brave seems to be doing, quite explicitly) seems like it deserves a very different evaluation under factor #1.
_fbpp 3y ago
The entire fair use claim is derived not from any legal basis, but rather, that "it has to be fair use" because it would be legally catastrophic for OpenAI et al if it weren't true. If you look at the core argument in favour of fair use, it's that "LLMs do not copy the training data", yet this is obviously false. For Github copilot and ChatGPT examples of it reciting large sections of training data are well known. Plenty can be found on HN. It doesn't generate a new valid windows serial key on the fly, it's memorized them. If one wants to be cynical, it's not hard to see OpenAI/etc patching in filters to remove copyrighted content from the output precisely because it's legally catastrophic for their "fair use" claim to have the model spit out copyrighted content. As this is both copyright infringement by itself, and evidence that no matter how the internals of these models work, they store some of the training data anyway.
- kmeisthax 3y ago
  OpenAI's bias research on DALL-E revealed that most examples of regurgitation come from repeated copies of the same image in the training set. When they filtered out duplicates, DALL-E stopped drawing training examples. The problem is that filtering the training set is naively O(n^2) and n is already extremely large for DALL-E. For LLMs, it's comically huge, plus now you have to do substring search. I've yet to hear OpenAI talk about training set deduplication in the context of LLMs. As for the legal basis... nobody's ruled on AI training sets in the US. Even the Google Books case that I've heard cited in the past (even by myself) really only talks about searching a large corpus of text. If OpenAI's GPT models were really just a powerful search engine and not intelligent at all, they'd actually be more legally protected. My money's still on "training is fair use", but that actually doesn't help OpenAI all that much either, because fair use is not transitive. Right now, such a ruling would mean that using AI art is Russian roulette: if your model regurgitates, the outputs are still infringing, even if the model is fair use. Novel outputs aren't entirely safe, though. A judge willing to commit the Butlerian Jihad[0] might even say that regurgitation does not matter and that all AI outputs are derivative works of the entire training set[1]. This logic would also apply in the EU. Last I checked the TDM exception only said training is legal, not that you could sell the outputs. They don't really respect jurisprudence the way the Anglosphere obsesses over "precedent", so copyright exceptions are almost always decided by legislatures and not judges over there, and the likelihood of a judge saying that all outputs are derivative works of the training set regardless of regurgitation is higher. [0] In the sci-fi novel Dune, the Butlerian Jihad is a galaxy-wide purge of all computer technology for reasons that are surprisingly pertinent to the AI art debate. Yes, this is also why /r/Dune banned AI art. No, I have not read Dune. [1] If the opinion was worded poorly this would mean that even human artists taking inspiration to produce legally distinct works would be violating copyright. The idea-expression divide would be entirely overthrown in favor of a dictatorship of the creative proletariat. [2] "Music and Film Industry Association of America" - an abbreviation coined for an April Fools joke article about the MPAA and RIAA merging together.
bewaretheirs 3y ago
Unpopular opinion time: A ML model is clearly a derivative work of its input. Here's what I think would be fair: Anyone who holds copyright in something used as part of a training corpus is owed a proportional share of the cash flow resulting from use of the resulting models. (Cash flow, not profits, because it's too easy to use accounting tricks to make profits disappear). In the case of intermediaries (e.g., social media like reddit & twitter) those intermediaries could take a cut before passing it on to the original authors. Obviously hellishly difficult to administer so it's unlikely to happen but I don't see a better answer.
- Dylan16807 3y ago
  > A ML model is clearly a derivative work of its input. Do you mean this in a copying sense or a mathematical sense? What if it's only storing 1 byte per input document?
- mattbee 3y ago
  I don't know what a fair settlement would be but I'm looking forward to a copyright-holder suing OpenAI to obtain one. These companies have no value if copyright can be enforced on their training data.
  - visarga 3y ago
    I think there are ways around it. The simplest would be to generate replacement data, for example by paraphrasing the original, or summarising, or turning it into question-answer pairs. In this new format it can serve as training data for a clean LLM. Of course the public domain data would be used directly, no need to go synthetic there. An important direction would be to train copyright attribution models, and diff-models to detect when a work is infringing on another, by direct comparison. They would be useful to filter both the training set and the model outputs.
    - mattbee 3y ago
      Would automated paraphrasing not be a derivative work of the original?

flangola7 3y ago

Microsoft is gambling on the hope that model training will be ruled fair use. This makes it seem that outcome is unlikely.

brookst 3y ago
Do you think a human learning something from reading is fair use? Or are we all copyright violators because reading that article altered our connectomes, and we may recall parts of it later?
- snickerbockers 3y ago
  Yes it is considered fair use but it's also completely irrelevant because we're talking about a computer program not a person.
- ethanbond 3y ago
  The point being raised is quite specific. Not sure if you’re willingly ignoring it or what? The answer is no, because you reading the article didn’t dramatically degrade its market value. An AI ingesting all content on the internet and then being ultra-effective at frontrunning that content for a large number of future readers does degrade its market value (and subsumes it into the model’s value).
  - cma 3y ago
    > The answer is no, because you reading the article didn’t dramatically degrade its market value. How about if you read a news article to write a competing one rewording and possibly citing it (one of the most common practices in news)?
    - ethanbond 3y ago
      How about it? Do you not think it incurs a lot of negative effects?
      - deleted 3y ago
        [deleted]
      - cma 3y ago
        What's the alternative, each news event is first to publish exclusivity regardless of quality? No synthesizing multiple stories into a linked narrative?
  - everforward 3y ago
    This applies to so many things, though. The most obvious parallel to me is YouTube. There are a ton of people ingesting books, then transforming that information into a roughly paraphrased video for people to watch for free (ish). That devalues the books they read and paraphrased, because other people don't need to read them. Spark Notes devalue actual books in a way, because a lot of high schoolers read those instead of buying the actual book. Search engines have also supplanted books in large part, because I don't need a whole book to answer a specific question. I don't know anyone that owns an encyclopedia anymore. This is the next iteration of these processes. Non-novel information's market value has been degrading for decades now. A series of questions that would have cost thousands of dollars in books to answer in the 70's/80's is now free, with or without AI.
    - llamaimperative 3y ago
      LLMs are attracting so much positive attention because they are likely to be a huge, huge step change improvement than all those methods you mention. For that same exact reason, it’s totally reasonable they’re attracting unique amounts of negative attention too. You can’t have it both ways: yes LLMs are going to change information retrieval the way nothing else has before, but no it’s actually just like all the other things in terms of their impact on incentive structures. FWIW I don’t really know where I land on this issue. I just find it totally incoherent to believe in the bull case of “this will transform everything” while also portraying it all as par for the course when discussing potential negatives. Just because Spark Notes didn’t obviously manage to kill valuable parts of our information ecosystem and economy does not mean that Spark Notes x 10,000,000 will not.

civilitty 3y ago

That’s not at all clear to me. IANAL but first of all it’s a balancing test, not a bright-line test. The judge could focus on any one factor and make an argument for either side quite easily. Second, “use” here could mean one of two things: training or inference. It’s publishing the results of inference that can lead to actual effects on the market, not the training. At the end of the day, someone has to prove tangible harm.

amluto 3y ago

I would look at #1 here. Crawling the Internet to collect information is one thing. (And people putting text on the web without requiring authentication seem to be granting at least some kind of license to anyone who sends a GET request.). But crawling the Internet (via centralized robots or users’ browsers), then storing that data and charging money to others for rights to that data (as Brave seems to be doing, quite explicitly) seems like it deserves a very different evaluation under factor #1.

_fbpp 3y ago

The entire fair use claim is derived not from any legal basis, but rather, that "it has to be fair use" because it would be legally catastrophic for OpenAI et al if it weren't true. If you look at the core argument in favour of fair use, it's that "LLMs do not copy the training data", yet this is obviously false. For Github copilot and ChatGPT examples of it reciting large sections of training data are well known. Plenty can be found on HN. It doesn't generate a new valid windows serial key on the fly, it's memorized them. If one wants to be cynical, it's not hard to see OpenAI/etc patching in filters to remove copyrighted content from the output precisely because it's legally catastrophic for their "fair use" claim to have the model spit out copyrighted content. As this is both copyright infringement by itself, and evidence that no matter how the internals of these models work, they store some of the training data anyway.

kmeisthax 3y ago
OpenAI's bias research on DALL-E revealed that most examples of regurgitation come from repeated copies of the same image in the training set. When they filtered out duplicates, DALL-E stopped drawing training examples. The problem is that filtering the training set is naively O(n^2) and n is already extremely large for DALL-E. For LLMs, it's comically huge, plus now you have to do substring search. I've yet to hear OpenAI talk about training set deduplication in the context of LLMs. As for the legal basis... nobody's ruled on AI training sets in the US. Even the Google Books case that I've heard cited in the past (even by myself) really only talks about searching a large corpus of text. If OpenAI's GPT models were really just a powerful search engine and not intelligent at all, they'd actually be more legally protected. My money's still on "training is fair use", but that actually doesn't help OpenAI all that much either, because fair use is not transitive. Right now, such a ruling would mean that using AI art is Russian roulette: if your model regurgitates, the outputs are still infringing, even if the model is fair use. Novel outputs aren't entirely safe, though. A judge willing to commit the Butlerian Jihad[0] might even say that regurgitation does not matter and that all AI outputs are derivative works of the entire training set[1]. This logic would also apply in the EU. Last I checked the TDM exception only said training is legal, not that you could sell the outputs. They don't really respect jurisprudence the way the Anglosphere obsesses over "precedent", so copyright exceptions are almost always decided by legislatures and not judges over there, and the likelihood of a judge saying that all outputs are derivative works of the training set regardless of regurgitation is higher. [0] In the sci-fi novel Dune, the Butlerian Jihad is a galaxy-wide purge of all computer technology for reasons that are surprisingly pertinent to the AI art debate. Yes, this is also why /r/Dune banned AI art. No, I have not read Dune. [1] If the opinion was worded poorly this would mean that even human artists taking inspiration to produce legally distinct works would be violating copyright. The idea-expression divide would be entirely overthrown in favor of a dictatorship of the creative proletariat. [2] "Music and Film Industry Association of America" - an abbreviation coined for an April Fools joke article about the MPAA and RIAA merging together.

bewaretheirs 3y ago

Unpopular opinion time: A ML model is clearly a derivative work of its input. Here's what I think would be fair: Anyone who holds copyright in something used as part of a training corpus is owed a proportional share of the cash flow resulting from use of the resulting models. (Cash flow, not profits, because it's too easy to use accounting tricks to make profits disappear). In the case of intermediaries (e.g., social media like reddit & twitter) those intermediaries could take a cut before passing it on to the original authors. Obviously hellishly difficult to administer so it's unlikely to happen but I don't see a better answer.

Dylan16807 3y ago
> A ML model is clearly a derivative work of its input. Do you mean this in a copying sense or a mathematical sense? What if it's only storing 1 byte per input document?
mattbee 3y ago
I don't know what a fair settlement would be but I'm looking forward to a copyright-holder suing OpenAI to obtain one. These companies have no value if copyright can be enforced on their training data.
- visarga 3y ago
  I think there are ways around it. The simplest would be to generate replacement data, for example by paraphrasing the original, or summarising, or turning it into question-answer pairs. In this new format it can serve as training data for a clean LLM. Of course the public domain data would be used directly, no need to go synthetic there. An important direction would be to train copyright attribution models, and diff-models to detect when a work is infringing on another, by direct comparison. They would be useful to filter both the training set and the model outputs.
  - mattbee 3y ago
    Would automated paraphrasing not be a derivative work of the original?