Amy Qi: Hello, I'm Amy from McGill University and with me is Jay Kerr-Wilson,
a partner in Fasken's Ottawa office and head of the firm's copyright practice.
Welcome to the first episode in our series, Perspectives, AI and Copyright:
Exploring the legal issues posed by adoption of generative artificial intelligence systems.
In today's episode, we're exploring the legal debate surrounding the use of copyright protected content to train
AI models and how courts and governments are responding to the conflicts between AI developers and content creators and
owners. For context. So we're all on the same page.
What exactly is machine learning and generative AI?
Jay Kerr-Wilson: Thanks, Amy. So artificial intelligence refers to an umbrella term for technology that's designed to perform a
human task. So, you've got a computer system or machinery that is doing something that until then,
humans had done. And, you know, we've had AI in their simplest form for a long time.
For example, stoplights and calculators are both examples of very simple AI that have been around for a long time.
Machine learning is a more complex version of artificial intelligence,
and it focuses on teaching algorithms to learn from data without being explicitly programmed.
So you set up a system that can analyze data and apply data to rules and to
learn patterns, and then to predict behaviour or future events based on those patterns that it has learned.
And really simple examples of machine learning that everyone is familiar with.
If you have a Spotify account or a Netflix account that recommends,
um, new shows for you to watch or new music for you to listen to based on your prior consumption.
So the AI has kept track of everything you've consumed on the platform,
and it says, oh, "Amy's very interested in historical drama".
And so then it will go through using that and predict other titles of,
um, of that genre that you might also enjoy.
The game changer that we've seen in the last few years is what's referred to as generative AI.
And this is the CHATGPT, for example.
What's different about generative AI is where machine learning could could apply a rule to a set of
data and then predict outcomes.
Generative AI is able to take a huge amount of data and learn the rules itself and then use
those rules to generate brand new data.
So ChatGPT, um, by reviewing huge amounts of examples of written documents,
is able to learn how documents are constructed, how humans write documents,
and then, and in fact write its own documents by applying what it's learned and creating something brand new.
So that's sort of the evolution from artificial intelligence through machine learning.
To now generative AI, which is what we're talking about today.
Amy Qi: You say that machine learning involves teaching algorithms to learn from data.
How specifically does generative AI use this data to train models?
Jay Kerr-Wilson: Word of caution I'm not a computer scientist, so this is going to be in very simple terms.
But what large language models, which are text based generative AI systems like ChatGPT are
able to do is they analyse hundreds of millions of documents,
examples of of written material, and they break that material down those documents down into
very small fragments of text, um, that are then called tokens.
And these tokens are then assigned numerical values.
What the system is able to do is it learns the relationships between tokens,
but at a huge scale. Hundreds of millions of times.
What words tend to follow these combination of words?
What letters tend to follow these combinations of letters?
And it runs through the system, and then it compares what it thinks it's learned to its data
set, which is the works it's learning from.
And then it makes adjustments, and then it does it again, and then it makes more
adjustments. Um, and by doing this, the by processing this huge amount of information over and
over and over again, many, many, many iterations, the the AI system is then in effect,
able to learn how to mimic human generated content.
So it's able to predict what a human might write in response to a given prompt.
And these things are all driven by prompts that a user gives it,
asking it to to write a specific, uh, piece of text.
And it can then mimic what, what it expects that a human would respond when given that that same prompt.
Amy Qi: Great. I think that makes sense.
Pivoting to the intellectual property landscape.
What does the existing copyright law in Canada look like in the context of AI and generative AI?
Jay Kerr-Wilson: So why the the discussion about generative AI has become so entangled with copyright,
not only in Canada but around the world, is because to train large language models,
you have to provide the system with access to hundreds of millions of copies.
And anytime you're making a copy of something, you're engaging copyright law of the jurisdiction you're in.
You know, ChatGPT has gone out and basically scoured the internet.
So websites, bulletin boards, looking for examples of articles and posts and social
media content and anything that's involved with written language.
And it has scraped all that content and ingested it into its training set that it's then using to do the training process
that I described, and all of that material, or virtually all of that material will be protected by
copyright. It's material that's owned by somebody, whoever the author or owner was.
And in most cases, these AI systems, large language models,
the people who are developing them have not asked for permission to use the content.
So they've simply scraped all this content and then used it to train the AI model.
So now we're at a stage where the various groups of owners of this content,
and whether it's authors of books, or people who take photographs,
or people who create artistic images are all starting to challenge the fact that their work
has been used without their permission.
In the training of these, Canada's Copyright Act has not been amended specifically to deal with generative AI yet.
But copyright law, like all laws, apply to AI.
So. So where there's copying taking place, then the Copyright Act would apply to those copies.
And in Canada, in most other jurisdictions, it's an infringement of copyright to make a copy of a work
without the owner's consent, unless there's an applicable exception.
And we'll talk about the exceptions a little bit later.
But the starting presumption is, is that unless the people who are developing these AI
systems can establish that there's an exception to copyright involved in the training of AI,
they are potentially liable for infringing copyright in a large amount of written material.
And why this is such a challenge to policy makers and to the industry is you can imagine that
if an AI system is trained using hundreds of millions of documents that will be owned by millions of people,
it's hard to conceive of a licensing system where the people who own the copyright in this content would get
paid anything more than fragments of a penny for each work, without creating such a huge cost on
AI developers that AI development will become impossible.
So when you're talking about the scale of hundreds of millions of documents, it's really hard to figure out what
is a price that we can put on this activity that will compensate authors more than a few pennies,
but still make the training of AI models financially feasible.
And so the the that's the tension and the debate that's going on right now.
Amy Qi: I see. Has there been any government response to address some of these issues raised by AI that you're talking about?
Jay Kerr-Wilson: Yeah. So it's interesting. In Canada, there was an initial consultation on whether or not the
Copyright Act should be amended to respond to the development of artificial intelligence.
And so stakeholders filed their submissions and the government reviewed them.
And this was 5 or 6 years ago, and the government came out and said,
well, we don't think it's necessary to amend the Copyright Act right now.
It's not an urgent situation. We're going to keep monitoring it.
Then shortly after that, we had the release of ChatGPT and a whole bunch of other AI
systems, both text based systems, image based systems, um, and they change the game very,
very quickly. So we started to see, um, a lot of development in the use of AI and a lot of
attention being paid on it, um, by copyright owners.
So the government launched a second consultation specifically to deal with,
uh, generative AI and the new developments that had come out.
And, you know, predictably, people who were from the technology sector.
People who are involved in the development or use of AI systems were suggesting that the Copyright Act should be
amended to include an exception to copyright.
What's often referred to as a text and data mining exception.
So an exception means that if you if you make a particular use of copyright material,
that the use is not an infringement of copyright.
And a lot of times there's conditions on that.
So the idea would being is if you're using material that's available on the internet that you've,
uh, scraped from the internet to train a large language model,
you would not need the consent of the people who own the copyright in that material in order to train your large
language model. And understandably, uh, the creative industries and people who own copyright in
material that's available over the internet Took a very different view,
and they were advocating that there should not be any exception that applies to development of AI systems.
They said that these companies had raised billions of dollars in investment to develop their AI.
They were starting to generate huge amounts of revenue from licensing their AI systems.
And it wasn't fair that these systems were being built on the backs of creators who were receiving no compensation for
the use of their works. That's where we are right now in Canada.
We've had this consultation. The government has received all of this,
all of these submissions from these various different perspectives.
But, you know, we had a federal election and we have a new government.
Obviously, the new government is facing a lot of other priorities on the trade front and on sort of foreign
relations with our largest neighbour.
Um, but at some point, I think they're going to have to come back and take another look at the at the development of AI
and try and come up with a policy that will then be reflected in amendments to the Copyright Act.
Amy Qi: I see. Um, you mentioned earlier that there's some exceptions for copyright infringement.
In Canada, there's a fair dealing exception to copyright for research.
What does this exception entail?
Jay Kerr-Wilson: So section 29 of the Copyright Act is what's known as the fair dealing exception.
And people may have also heard uh, the term fair use.
So fair use describes the sort of the, the system in Canada or the exception in Canada or in the
United States, rather. So in the United States, it's referred to as fair use.
In Canada we refer to it as fair dealing.
And so fair dealing applies to a very specific list of uses,
um, like research, like education, uh, like parody and satire,
um, news reporting. And if you're using copyright material for one of these very specific purposes and your use is
fair, then you don't need the permission of the copyright owner and you don't need to pay compensation.
So one of the very specific uses of fair dealing is fair dealing for the purpose of research.
Uh, so the argument would be that training large language models and other types of generative AI systems is research.
So you could use then copyright material, um, for the training of AI as long as the
use was fair. And whether or not the use is fair is a very fact specific examination that courts will go
through in infringement proceedings where fair dealing has been raised as a defence.
And so they will look at, you know, what is the purpose of the dealing if it's for research or
is it for some other purpose. What is the amount of the dealings or are you dealing with the whole work,
or are you dealing with just part of the work?
What is the nature of the work?
Is this a work that's already widely available?
Is this a work that is confidential?
And one of the key ones in the debate around AI is what is the effect of the dealing on the work?
So if you're involved in fair dealing for the purpose of research,
does that research or that dealing diminish the economic value of the work to the owner?
We haven't had. So there's been a number of cases that have been started in Canada,
but we haven't had any decisions yet.
But that's that's how fair dealing for the purpose of, of research might apply.
Another exception that I think courts will want to look at in the context of generative AI is Canada has a specific
exception that covers temporary reproductions for a technological purpose.
So if you have to make a very brief copy of a work or a reproduction of a work for a temporary purpose,
and it's it's in order to facilitate some technological purpose.
And then once that technological purpose has been fulfilled,
the copy is destroyed. Then that's also another exception to copyright that could apply to to the
use of copyright materials to train AI systems.
Amy Qi: You mentioned that in the US, this concept of fair dealing is called fair use in section
107 of the Copyright Act. It outlines the four factors for determining fair use.
Can you briefly explain this criteria as well?
Jay Kerr-Wilson: Sure. And there is a lot of overlap between the fair use analysis in the US and the fair dealing analysis in Canada.
But there are some differences. And one of the big differences in the US,
if you want to make fair use of somebody else's work, then your use has to be what's known as transformative.
So you have to use the work to, in effect, create something new.
And that's a requirement of the use being fair in the US And in Canada,
we don't have that same factor in our fair dealing analysis.
Whether or not the dealing is transformative isn't something that courts will look at,
but in many other ways the two analysis are very similar.
So courts in the US will look at whether or not the use has a prejudicial impact on the market value for
the copyrighted work. Um, what is the nature of the work?
Is the posed fair use for a commercial purpose, a non-commercial purpose?
So it's similar. But the big difference is this requirement in the US that the use be transformative.
Amy Qi: Okay, great. Now that we understand some of the tech and legal background associated with AI training data,
I think it's interesting to see how the theory is applied in court.
Recently, there have been several landmark cases in court decisions pertaining to the use of copyrighted material to
train AI models that we've been discussing.
Two notable cases are the lawsuits that both Anthropic and Meta are facing in the San Francisco US District Court,
starting with Anthropic. Could you give us a summary of the context for this case?
Jay Kerr-Wilson: Sure. So Anthropic is an AI company that's developed, uh, a family of large language models that it's named
Claude. Uh, so it works similar to ChatGPT.
And in order to train their large language model, anthropic really focused on using books in their training
set. They had determined that, you know, books by their very nature,
um, provide much better training content than blog posts or articles or much smaller pieces of
text. Books tend to be much better written.
They tend to be more complex. Uh, they express sort of more comprehensive thoughts.
So if you want to train a system about how to predict and produce high value Literary
content. The the idea was that books provides the ideal training set.
So anthropic built its training data set from two sources.
So it licensed a large amount of copyrighted books.
So it actually purchased books.
And then it scanned these books into digital form and fed them into its training set.
But these were books that it had bought and paid for, but it had also was alleged that it had downloaded
millions of copies of copyrighted books from what are known as pirate sites on the internet,
so large files that contained millions and millions of digital copies of books for which the publishers and authors
had never given their consent for the copies to be made.
Anthropic used both of these sort of sources for books, for literary materials to then train its Claude large
language model. And there was what was known as a class action.
So a class action lawsuit is where you have one plaintiff who represents a whole class of similarly situated
plaintiffs. So in this case, we had an author was suing Anthropic in California for the unauthorised use of his
book, but his litigation would also cover, uh, all authors in the class whose books had then been used
by Anthropic uh, without consent.
Amy Qi: And what was the court's decision in this case?
Jay Kerr-Wilson: So there was a couple different layers of the court's decision.
And again, it depends. It turned on the fact that there was the two sources of data.
So there was the the books that anthropic had bought.
And then there was the. Scanned digital copies that it had acquired without the author's consent.
And the fact that Anthropic. In addition to training its large language model,
Claude. It was also building a central library of as many literary works as it could.
So it wanted to both house a central library that would then persist of books,
but also train its large language model.
So the court on the on the training data, it did the fair use analysis and it said absolutely that
training large language models and other AI systems was transformative.
Uh, it found that, um, the that there was no impact on the commercial value of the works that
were used in the training model because Anthropic had put safeguards in place.
So if you're using Claude, the large language model, you couldn't ask Claude to simply reproduce one of the books
that was in the training data.
Claude would not. Was designed not to reproduce a verbatim a book that it had that had been included in its system.
It would only generate brand new content.
So the court concluded, well, you know, Claude's not going to produce copies of Catcher in
the Rye that will then compete with the original Catcher in the Rye that's included in the in the training set.
So, the court concluded that that based on its fair use analysis,
training LLMs in the way that that Anthropic was doing, it was fair use and therefore not infringement
of copyright. On the issue of maintaining this large, persistent digital library of books.
The court said anthropic had a right to keep digital copies of the books it had legitimately purchased and scanned
because it owned those books, but it didn't have a right to keep in this large collection
the pirated copies of the books.
So the court ultimately found that there was liability for the reproduction of the copyright material in the persistent
library, but that fair use covered the training model, which was sort of the big takeaway out of that case.
Amy Qi: I know you've already briefly touched on this, but just to get into more specifics.
How were the four factors that we've talked about before of American copyright law for fair use evaluated in this case?
Jay Kerr-Wilson: Sure. And just to sort of go through the list, so the the, the purpose and character of the use in this
case was the training using the the copies of the books to train large language model,
which the court had no trouble finding that the the purpose was transformative because,
um, of the process of breaking the works down into the small fragments and the fact that you're producing something new
at the end, the fact that the author's works were creative worked against,
uh, anthropic on the nature of the of the works.
Uh, but the amount of the use the court actually found in anthropic favour,
because one of the things about training large language models is you want large language models to produce content
that is as free of bias as possible and as, you know, accurate as possible.
And you want to avoid AI systems that are, you know, hallucinate.
And in order to to generate the best possible outcomes, you have to have as much material from as many different
sources as you can. So in this case, the court said, in fact,
the amount of material that anthropic was using worked in favour of fair use because it made for better outcomes.
And then on the effect of the market, the court found in favour of Anthropic because they said,
you know, there was no the plaintiffs could not establish that Anthropic was producing copies that were
competing directly with the works that were in the training set.
Amy Qi: Very recently, Anthropic has actually announced that it has reached a preliminary settlement in this class action
lawsuit. In a court filing on August 26th, 2025.
What does this mean and what are the impacts of the settlement?
Jay Kerr-Wilson: So, as I said, although Anthropic was successful on the fair use question,
it um, uh for the training, it was not successful on the sort of the large collection,
the library it had built. And because of the number ofindividual books that were in this library for which there
was potential liability, Anthropic was facing a huge potential financial hit from damages.
So, you know, this was just a way to not have to go through the process of assessing the damages in an unnecessary
trial. So the settlement then sort of puts the question of damages at an end.
Amy Qi: Right. And the other case that we mentioned earlier involves Meta,
and I think it serves as an interesting comparative to the Anthropic one,
because they both occurred in the same court and it has similar facts to the anthropic case.
So could you give a summary of the context for this Meta case?
Jay Kerr-Wilson: Sure. So Meta, as most people know, is the company that operates social media platforms such as
Facebook and Instagram. But it's also developing its own large language models called llama.
Similar to the Anthropic case, Meta had used a very large volume of books for which it had
not obtained the consent of the publishers of the authors in order to train its models.
And so, again, it was a class action.
So there was a representative plaintiff author who represented a class of authors,
and they were claiming copyright infringement because of the copies that were used to train the large language model.
Amy Qi: And what was the court's decision in this case?
Jay Kerr-Wilson: This is an interesting outcome.
So the court ultimately so so what had happened as Meta had brought a motion for summary judgement,
and the court granted Meta's motion.
So so Meta ultimately won the case on the summary judgement motion.
But what was really interesting was the court did the same kind of fair use analysis that the court in Anthropic had
done, and came to almost an opposite result, and largely on the question of what is the impact of large
language models on the market for published books, and why this is particularly interesting is this is the
exact same court, same district court in the US and California,
different judges. And these decisions were issued just a few days apart.
And the judge in the Meta decision, in fact, explicitly references and disagrees with the
decision of the same court in the anthropic case.
So on this question of what is the effect of large language models on the copyright works or the books that are used in
training? The court in the Anthropic case said, um, well, anthropic large language model.
Claude isn't producing copies of the books, so it's not competing with the books in the training set
because it's not reproducing those books, it's producing new books.
So there's no effect on the value of catcher in the Rye.
When the large language model produces brand new books or new written texts that aren't catcher in the Rye.
So it took a very narrow view of the analysis of what is the impact on the on the market for books.
The court in the Meta decision took a very different view.
It took a very broad view of what is the impact of large language models on the publishing industry in general,
and the court was very concerned that because large language models are able to crank out thousands or
millions of books very quickly, very cheaply, um, that are,
you know, based on what it has learned from the published works,
it would overwhelm the publishing market.
So human authors will have a hard time being able to make a living,
because they're then going to be competing against all of these mass produced AI copies that are going to overwhelm
the publishing industry. So it's a very different from a very narrow perspective,
you know, will the individual author be harmed versus a very broad policy based perspective in the meta court that says,
you know, the publishing industry, in fact, is in peril or human.
The human element of the publishing industry is in peril if AI is allowed to run unchecked.
We haven't seen the end of this debate in the US, and there is very similar class action lawsuits that have
been commenced in Canada. We don't have any decisions in Canada yet,
but this the the court battle over the use of AI is just beginning.
It's far from over.
Amy Qi: You mentioned Canada there at the end.
Um, how do you think these cases could be evaluated if they were transposed to a Canadian copyright law context?
Jay Kerr-Wilson: So it's interesting because Canada tends to have, under its fair dealing,
uh, analysis results that are more user friendly than the United States.
So we've had decisions of the Supreme Court of Canada that said,
fair dealing isn't just a technical exception to copyright.
It's actually a user's right. So this is how fair dealing has been constructed,
as Canada and the court has told us that copyright has to be analysed as a balance between the interests of the user,
the public interest and the interest of the owner.
So although we haven't seen any cases yet, and I think there's lots of good arguments on both sides,
my prediction would be that an analysis of a Canadian court would likely be closer to the anthropic,
uh, analysis, that it would take a very narrow view of what is the impact of the use or the dealing on the particular
work at issue, and not take the broad policy based approach that the court did in the meta case?
Amy Qi: That makes sense. Um, just to end off the episode with a more general question,
where do we go from here?
Jay Kerr-Wilson: So these issues need to be resolved.
I think everyone understands that, you know, the generative AI genie's out of the bottle.
It's not going back in. Um, all industries are starting to adopt AI solutions and AI technologies,
so there needs to be some sort of resolution to these issues,
and it's going to have to be a policy based resolution.
So ultimately, I think there's going to have to be legislative amendments.
We may get some court decisions before that happens.
And oftentimes court decisions will inform what governments will do with legislative amendments.
The challenge is it takes a long time for these cases to go through the court system,
and it takes a long time for governments to pass legislation to amend the Copyright Act to deal with these emerging
technologies. And the technology's going really fast.
So even if we figured out a way to, you know, resolve the issues that are confronting us today.
You know, three years from now, we're probably going to have a whole different set of
challenges that will also have to be addressed.
Amy Qi: Okay. I think that's a good note to end on.
Thank you to Jay for your insight on generative AI and the legal issues associated with it.
As we've seen, there's a lot of nuance involved, but I think you really helped clarify the legal debate on
the potential copyright infringement involved in AI training data.
Thank you for listening to this episode of Perspectives and Copyright,
and make sure to tune in for the next one.