Below (and attached) is the archival community response to the Federal Government Consultation on Copyright in the Age of Generative Artificial Intelligence, submitted by the Canadian Council of Archives and endorsed by the Association of Canadian Archivists and l’Association des archivistes du Québec.  A French translation of the submission will be posted when it becomes available.

The submission was prepared by the Canadian Council of Archives (CCA) Statutory Review Working Group - established to examine and address issues related to the Canadian archival community in the review of the Canadian Copyright Act, and the ongoing consultations instituted by the federal government. The Working Group is composed of: Nancy Marrelli, Chair; Jean Dryden, appointed by the Association of Canadian Archivists; Nancy Marrelli is also Interim representative of l’Association des archivistes du Québec.

The consultation submissions required responses to an online questionnaire, so the attached document replicates the questions and our responses. Some questions were not applicable to our community and they have been tagged as Not Applicable (NA)

Copyright and Generative AI Consultation
Questionnaire Responses from the Canadian Archival Community

January 14, 2024
Technical Evidence

The Government of Canada invites views on technical aspects of AI technologies, including on the following questions:

• How does your organization access and collect copyright-protected
content, and encode it in training datasets?

NA (not applicable)

• How does your organization use training datasets to develop AI
systems?

NA

• In your area of knowledge or organization, what measures are taken to
mitigate liability risks regarding AI-generated content infringing existing
copyright-protected works?

NA

• In your area of knowledge or organization, what is the involvement of
humans in the development of AI systems?

NA

• How do businesses and consumers use AI systems and AI-assisted and AI-
generated content in your area of knowledge, work, or organization?

The holdings in Libraries, Archives and Museums (LAMs) are a major source of documents for AI researchers in their development of training datasets for use in training AI models, particularly those datasets used to train large language models. Canadian archival holdings are a rich treasure trove of records in all formats that serve as the raw material for scholars, students, and ordinary citizens. Archival institutions have eagerly embraced the opportunities provided by the Internet to digitize and make our holdings available online. When digitized, traditional records can be mined for valuable historical information. For example, fur traders’ journals document decades of weather patterns, relations between indigenous people and settlers, and early commercial activities; and tax rolls record the names of residents, which are of great interest to family historians. In addition, archival institutions are acquiring born-digital records and research data
sets from their parent institutions and private donors.

Transparency and ensuring non-bias in training datasets is of concern to LAMs because of our public service mission. Although it may be onerous, it is very desirable to create the metadata to be able to identify and link the training datasets to the generative AI output created by the AI tool. This metadata will provide transparency and oversight to the users, and also for the creators and rightsholders whose works are used in the datasets in a non-consumptive way. Canadian LAMS are also beginning to use some of the emerging generative AI tools for their own purposes. Archives can use AI tools to generate basic metadata, transcriptions, and create important access points for all manner of digitized documents, thereby providing greatly improved access to our holdings for their researchers and the General Public. (See Pavis, Mathilde. Artificial Intelligence: a digital heritage leadership briefing, 2023 https://www.heritagefund.org.uk/about/insight/research/artificial-intelligence-digital-heritage-leadership-briefing.). Along with many other materials, Canadian archives include a large volume of orphan works (works for which the rights holder is unknown or unreachable) on a wide variety of topics and archivists and researchers would benefit greatly from more clarity on whether or not orphan works can be digitized to enable this kind of improved access. The newly created metadata that can be produced by generative AI will unlock access to the content of archival holdings that goes well beyond what is possible with the limited human resources currently available in archival institutions.

Recommendations:
• Provide clarity in the Copyright Act for the legal use of data for training
generative AI tools
• Require AI researchers and developers to ensure training datasets have
identifiable metadata that can be linked to generative AI output

Text and Data Mining

The Government of Canada invites views on whether any clarification is needed on how the copyright framework applies to text and data mining (TDM) activities, notably on how and when rights holders could or should be compensated for the use of copyright-protected content as inputs in the development of AI. Although all comments are welcomed, the Government is particularly interested in receiving feedback on the following questions:

• What would more clarity around copyright and TDM in Canada mean for
the AI industry and the creative industry?

NA

• Are TDM activities being conducted in Canada? Why or why not?

NA

• Are rights holders facing challenges in licensing their works for TDM
activities? If so, what is the nature and extent of those challenges?

NA

• What kind of copyright licenses for TDM activities are available, and do
these licenses meet the needs of those conducting TDM activities?

NA

• If the Government were to amend the Act to clarify the scope of
permissible TDM activities, what should be its scope and safeguards?

Libraries, archives, and museums (LAMs) are unlikely to be AI developers, so
LAMs don’t need an exception that permits them to use copies to train machines. LAMs are more likely to be asked to make copies of their holdings for AI developers upon request. If so, the fair dealing provision (s 29) of the Copyright Act (CA) may well serve, with some changes. The changes proposed below build upon provisions that are part of the balance between the rights of copyright owners and the interests of users already established within the CA. Before describing the proposed changes, it is important to note that provisions such as fair dealing and the exceptions for LAMs are fundamental to the balance inherent in a well-functioning copyright system. Canada’s approach to the challenges of AI must begin with established principles. The Supreme Court of Canada (SCC) has established that exceptions are not just loopholes, but users’ rights (CCH v LSUC 2004 SCC 13 para 48), and we steadfastly defend their presence (particularly the fair dealing provision) as a fundamental principle. Since fair dealing is not limited to particular user groups, rights, formats, or categories of
protected matter, everyone can benefit from it to access and use copyrighted material without authorization or payment, provided that the dealing is fair as determined by the SCC’s two-step test.

As beneficiaries of fair dealing, LAMs already can make copies upon request for the purpose of research. Provided that TDM is appropriately defined to be clear that it is included within a broad and liberal interpretation of research, making copies for TDM falls within one of the allowable purposes of fair dealing. That uncertainty would be clarified if the fair dealing provision were amended by adding TDM or computational data analysis to the list of authorized purposes, OR by making the purposes illustrative rather than exhaustive, i.e., “fair dealing for purposes such as research, private study, ...do not infringe copyright.” A further condition would require the LAM to inform the requester that the copies were provided for research only, that any further uses may require the permission of the rights holder, and that it is the responsibility of the requester to obtain any necessary permissions. Admittedly, the scope of fair dealing may have to be clarified through litigation, since the limited case law cited in the consultation paper does not address situations where the copied images were used to train a
machine.

Since users’ rights are fundamental to a balanced copyright system, constraining them through contractual agreements undermines the system. Thus, the Copyright Act must be amended to provide that any contractual provision contrary to the exceptions in the Act shall be unenforceable.

The proposed amendments would provide legal clarity for both LAMs and AI
developers by enabling LAMs to provide copies to AI developers to be used in the training of machine learning models.

Recommendations:
• Amend the fair dealing provision of the Copyright Act to provide that TDM lies within the scope of fair dealing.
• Amend the Copyright Act to provide that copyright exceptions cannot be
overridden by contract terms.

• What would be the expected impact of such an exception on your industry
and activities?

NA

• Should there be any obligations on AI developers to keep records of or
disclose what copyright-protected content was used in the training of AI
systems?

Having sufficient metadata that would identify and link the training datasets to the generative AI output created by the AI tool would be highly desirable.
Requiring AI developers to provide such metadata would provide transparency to the users of AI tools, and to the creators and rightsholders whose works are used in the datasets in a non-consumptive way. In order to ensure transparency and clarify rights issues, generative AI output should always be tagged as such.

Recommendation
• Require AI researchers and developers to ensure training datasets have
identifiable metadata that can be linked to generative AI output.
• Generative AI output should always be tagged as such.

What level of remuneration would be appropriate for the use of a given work in TDM activities?

NA

• Are there TDM approaches in other jurisdictions that could inform a
Canadian consideration of this issue?

The possibility of a more general exception to permit TDM falls outside the scope of the archival community’s direct interests. If, however, such an exception is needed, the provisions of Singapore’s Copyright Act pertaining to computational data analysis (sections 243-244) are well thought out in terms of scope and appropriate safeguards.

Its strengths are:
• Definition of “computational data analysis” (s. 243)
• Limited purpose (only Computational data analysis) (s. 244)(2)(a) & (b))
• Copy supplied/communicated to another only in very limited circumstances (s.244)(2)(c) & 244)(4))
• User must have lawful access to source materials (s. 244)(2)(d))
• Infringing source materials can be used subject to specific limited conditions (s.244)(2)(e))


Authorship and Ownership of Works Generated by AI

The Government of Canada invites views on how the copyright framework should apply to AI-assisted and AI-generated content. Although all comments are welcomed, the Government is particularly interested in receiving feedback on the following questions:

• Is the uncertainty surrounding authorship or ownership of AI-assisted and
AI-generated works and other subject matter impacting the development
and adoption of AI technologies? If so, how?

In Canada the basic principle is well established that copyright is automatic
for original creations that include human skill and judgment, as specified
in the Supreme Court decision (CCH Canadian Ltd. v. Law Society of Upper
Canada, 2004 SCC 13, para. 25). The output that results from the generative
AI mechanical process cannot meet this requirement for skill and
judgment and is therefore not protected by copyright. The humanly
created algorithm does meet the requirement and is protected by
copyright.

The current principle of not assigning copyright protection to generative AI
output does not at all appear to be limiting the rapid development and
adoption of AI technologies. The lack of certainty is, however, having a
profound effect on creators and how they view their future prospects from
both an economic and social standpoint.

• Should the Government propose any clarification or modification of the
copyright ownership and authorship regimes in light of AI-assisted or AI-
generated works? If so, how?

We believe that assigning full intellectual property rights to the output of
generative AI processes is inappropriate. LAMs have a long history of
advocating for clarity in the Copyright Act and we believe this issue must be
addressed in the legislation, to provide as much clarity as possible.
Even with the current constraints and uncertainties, AI is profoundly
disruptive in many ways, particularly to the creative communities. Assigning
copyright protection to AI output would very negatively affect the work of
creators and their contribution to society, resulting in a negative effect on
incentive to create. Extending copyright protection to AI output calls into
question the value we place on human creativity and expression.
AI processes can be programmed to create mass output that could quickly
monopolize the creative space, thereby disrupting in profound ways human
creative activity, the copyright balance, and the marketplace.

The rapid development and dissemination of AI has already created
considerable disruptions to the creator community, and these will continue
to be a major problem. Creators contest that the ingest of their works in
the creation of the AI training models without attribution, permission, or
financial compensation is a serious problem that will affect them in many
ways. But fair dealing and/or a TDM exemption would permit data mining
for research purposes of the millions or billions of documents in the data
sets used for training.

The prospect of directly compensating creators within the structure of the
Copyright Act raises many thorny problems. Copyright law should not be
used to address broad societal problems and challenges. However,
copyright law is not the only way that we can reward creators. We
recommend that the Government create a system outside the copyright
regime to reward and acknowledge creators for the part their work plays in
generative AI, such as a program in which AI developers are required to
contribute to a fund that will be plowed back into the creator community to
support a broad spectrum of Canadian creativity. (Other examples of this
type of scheme are Canada’s Public Lending Rights, Telefilm). The details of
how such a program would work would have to be carefully considered,
with input from the creator community, and the outcomes would have to
include mandatory contributions by those developing the training datasets,
and money paid out to the creator community. This would help redress the
balance between human creators and the potential dominance of large
corporate AI in the marketplace and the creation landscape. Such a
program would enhance Government efforts to ensure support for
Canadian creators and creative industries, while simultaneously fostering
Canadian AI competitiveness, innovation, and support for maintaining
overall access to Canadian creation, all of which are important public policy
objectives.

Recommendations:
• Amend the Copyright Act to maintain and clarify the basic principle that
copyright protects original creations that are the product of human skill and
judgment and that the mechanical generative AI output is in the public
domain, but the humanly created algorithm is protected by copyright.
• Create a system outside the copyright regime, that rewards and
acknowledges creators for the part their work plays in generative AI, whereby
generative AI developers are required to contribute to a fund that will be
plowed back into the creator community to support a broad spectrum of
Canadian creativity.


Are there approaches in other jurisdictions that could inform a Canadian
consideration of this issue?

With further study and careful consideration, it is possible to consider very
limited rights for AI outputs in particular circumstances, a variation of what
is sometimes referred to as “thin copyright”, such as the limited rights
sometimes accorded to databases. But these should be very limited in both
scope and duration.

Recommendation:
• Consider very limited rights for AI outputs in particular circumstances, a
variation of what is sometimes referred to as “thin copyright”.

Infringement and Liability regarding AI

The Government of Canada invites views on questions about copyright
infringement and liability raised by AI, particularly since there is a lack of evidence currently available in this regard. Although all comments are welcomed, the Government is particularly interested in receiving feedback on the following questions:

• Are there concerns about existing legal tests for demonstrating that an AI-
generated work infringes copyright (e.g., AI-generated works including
complete reproductions or a substantial part of the works that were used in
TDM, licensed or otherwise)?

NA

What are the barriers to determining whether an AI system accessed or
copied a specific copyright-protected content when generating an infringing
output?

At present, there is no requirement for AI developers to provide metadata that would identify and link the training datasets to the generative AI output created by the AI tool. Requiring AI developers to provide such metadata would assist in determining whether protected material had been copied when generating an infringing output, in addition to providing transparency to users of AI tools, and to the creators and rightsholders whose works are used in the datasets.

Recommendations
• Require AI researchers and developers to ensure training datasets have
identifiable metadata that can be linked to generative AI output.

• When commercialising AI applications, what measures are businesses taking to mitigate risks of liability for infringing AI-generated works?

NA

• Should there be greater clarity on where liability lies when AI-generated
works infringe existing copyright-protected works?

It is clear that there should be greater clarity on where liability lies when AI-
generated works infringe copyright-protected works. The current liabilities for copyright infringement should apply, but the issues will be clarified through litigation. Resolving the potential continuum of responsibility that arises with the actual situations in the litigation will be a more realistic approach, rather than rushing into a legislative solution that may have unintended consequences. The solutions must be consistent with the public policy issues discussed in other sections of this questionnaire

Recommendations
• Continue to apply current liability provisions and remedies for copyright
infringement.
• Resolve liability and remedies issues that arise with ongoing litigation, to be consistent with sound public policy

• Are there approaches in other jurisdictions that could inform a Canadian
consideration of this issue?

NA