# A WINDOW INTO GENERATIVE ARTIFICIAL INTELLIGENCE UNDER COPYRIGHT LAW & POLICY IN CANADA

## Abstract

Generative artificial intelligence (GenAI) challenges and alters existing sociotechnical practices and regulatory schemes. This research provides a window into the current Canadian copyright law and policy context concerning GenAI, offering insights derived from existing legal precedent and policymaking efforts. Key issues addressed include the varied nature of policymaking processes, the copyright implications of text and data mining (TDM) and GenAI training models, the evolving roles of licensing and data curation, and ethical considerations around transparency. This work offers perspective on the current state of Canadian copyright law regarding AI and provides guidance on where future policymaking efforts and reforms are most needed.

## Introduction

Emerging technologies challenge existing regimes. Copyright law is rarely spared from the disruptions, tensions, and controversies that attend technological innovation and sociotechnical change. The movable type printing press, player piano, photocopier, VCR, search engine, DVR, peer-to-peer filesharing, mass digitization, and content streaming platforms are familiar touchstones in a genealogy sociotechnical change that now also includes generative artificial intelligence (GenAI). Emerging technologies challenge, shift, and perhaps upend existing ways of creating, working, sharing, collaborating, understanding, and participating in the world. History teaches that sociotechnical controversies are a normal, likely unavoidable, perhaps desirable part of sociotechnical change.

The perniciousness of tech-law controversies may be understood, in part, as a natural consequence of the disconnect between processes of technological innovation and institutions of legal fixedness. Processes of technological innovation tend to be relatively fast-paced, forward-looking, and concerned with novelty and newness. Legal institutions and rules, by contrast, tend to be relatively change-resistant and are, by design, prone to inertia. Lawyers and judges employ retrospective logics, bound by precedent that privilege the status-quo over change. This steadfast character of law is an important source of its legitimacy and authority in society. Policymakers operate in an important middle space between technological change and legal stricture. Policy, in its ideal form, is a critical lever that weaves sober reflections of the past with future windows of possibility, moderating the dynamic and potentially upending process of sociotechnical transformation.

GenAI is the latest tech controversy dominating copyright debates and discourse in Canada. Researchers and policymakers are trying to contend with the ongoing and evolving risks AI systems pose to the security and safety of institutions critical to democracy including a free
press, public education, fair elections, and virtually all aspects of governmental operations and public sector activity. Within the relatively narrower domain of intellectual and cultural production in Canada, GenAI is actively disrupting norms and expectations around productivity and innovation, creativity and collaboration, expression and transformation, transparency and consent, reliability and trust, equity and fairness. GenAI is a forcing function -- we must define our values as a society and confront how our values are shaping and being shaped by GenAI.

This work offers perspective on the current state of Canadian copyright law and policy regarding AI and provides guidance on where future jurisprudence and policy reforms are most needed. Current and ongoing efforts by the Canadian government and others to better understand the copyright implications of GenAI are discussed. Next, key issues and findings relating to particular concerns around text and data mining, authorship, ownership, liability, attribution, consent, and licensing are distilled. Some comparisons are made to the legal and policy context in United States where it is germane to do so. The paper concludes with reasoned speculation about how copyright law and policy might regard GenAI in the near-to-midterm and offers a few evidence-based considerations and recommendations moving forward.

## Current Efforts in Context

The Canadian government's approach to copyright and GenAI has, thus far, been to facilitate various public consultations and stakeholder listening sessions. For purposes of this research, two recent or ongoing initiatives are particularly relevant and will be discussed in the following sections. First though, it is worth acknowledging that Canadian lawmaking and policymaking process around AI and related concerns (more generally than the copyright-specific ones discussed below) occur at a slower pace than some of our counterparts. This "leisurely" pace results from several factors but, at present, results primarily from shifts in leadership. Canadian Parliament was prorogued following Prime Minister Trudeau's decision to step down, and it remained in stasis as a new leader was chosen and the nation prepared for the next federal election.

## The Pregnant Pause of Prorogation

During this moment of political pause and reorganization, pending bills (on AI, privacy, data interoperability, and more) "die" on the Order Paper. Seated committees lose their power to transact business. Governmental departments also temporarily suspect their efforts; formal policy consultations and other public solicitations for participation freeze as regulators and bureaucrats brace for the impending political shifts and await the government's resettling. After the federal election, members of government decide which legislative priorities they will undertake, and whether to revisit earlier-sought reforms and/or put forward new bills for consideration.

The moment of pause gives the new government a fresh start, but it is not an empty pause. While formal processes are stymied, informal and *de facto* policymaking processes continue unabated. Informal policymaking processes often include active participation by members of industry, public sector stakeholders, lobbyists, academic researchers and independent scholars, and public interest organizations. These actors advocate for and advance
agendas, perceptions, discourse, and debates around active policy controversies through informal or unofficial channels. Informal forms of policy intervention often include: influencing policymakers through lobbying; colouring public opinion through news media reporting; conducting research on open questions to gain new insights or evidence into the evolving landscape, and publishing articles and reports that clarify the state of the art or the state of understandings; engaging in exploratory townhalls, roundtables, or focus groups to better understand the experiences and desires of stakeholders; and coalition-building within or amongst different industries or sectors, and/or within grassroots or other public sector communities.
Lawsuits and other formal legal proceedings may also be initiated as a way of instigating broader policy reforms through judicial mechanisms (discussed further below).

If viewed in a positive light, the political pause in federal rulemaking gives these informal modes of influence and policymaking room to develop and grow, empowering participants to be well-positioned to effect desired policy reforms when the federal government resumes its work. A more pragmatic and critical perspective is that the tendency of informal policymaking to step in when formal processes stop offloads policy work onto non-governmental actors who may be ill-equipped to respond to the challenges. As a result, uncertainty may proliferate where clarity is needed, and a series of incoherent and uncoordinated initiatives based upon niche interests may flourish instead of a harmonized set of principles and priorities that reflect the values and needs of society.

## Formal Participatory Policymaking Processes

The Canadian government's consideration of GenAI in light of copyright law and policy has, thus far, focused on facilitating various public consultations and stakeholder listening sessions. Two recent or ongoing initiatives are particularly relevant and will be discussed in turn: "Consultation on Copyright in the Age of Generative Artificial Intelligence" and "Cultural Data Strategy for Artificial Intelligence".

"Consultation on Copyright in the Age of Generative Artificial Intelligence"
First announced in October 2023, the "Consultation on Copyright in the Age of Generative Artificial Intelligence" was jointly undertaken by the Ministry of Innovation, Science, and Economic Development Canada (ISED) and the Ministry of Canadian Heritage (ISED, 2023). This consultation was part of a follow-up to the government's 2021 "Consultation on a Modern Copyright Framework for Artificial Intelligence and the Internet of Things" (Government of Canada, 2021). While its predecessor had a farther-ranging and different aim — facilitating the drafting of an overarching federal bill on AI (the now-defunct *Artificial Intelligence and Data Act* (AIDA) Bill C-27 (Government of Canada, 2022)) — the consultation at issue here focused specifically on the copyright implications of GenAI in consideration and anticipation of future copyright policy developments on AI.

The Consultation was open for public comment between October 12, 2023, and January 15, 2024 (excepting two late-received submissions) and received a total of one hundred and three submissions from a range of respondents including technology firms, cultural sector
organizations, lobby groups, educational sector organizations, coalitions of academic researchers, individual scholars, and representatives of the G.L.A.M. sector (galleries, libraries, archives, and museums) (ISED, 2024(b)). Demographic information on industry and organizational affiliation (where appropriate) and the location of participants was collected. Substantively, the Consultation consisted of a series of survey questions that seemed to be aimed at soliciting responses to two main areas of interest.

Several questions on the survey where quite technical and seemed focused on how members of industry were developing and using AI systems. Such questions solicited information on how respondents' access and collect copyright-protected content and encode it in training datasets, how datasets are used to develop AI systems, what measures are taken to mitigate potential copyright liability related to GenAI content, the extent of human involvement in the development of AI systems, and how businesses or consumers use AI in respondents' field or organization (ISED, 2024(a)). These more technical or "technology-in-use" responses are important but fall beyond the purview of the present research.

The other substantive category of survey questions was interested in more obviously copyright-related inquiries. These questions were oriented around on three themes: (1) the use of copyrighted works in AI training models, (2) authorship and ownership of AI-generated content, and (3) liability stemming from GenAI, particularly with respect to potentially infringing outputs. Findings from this part of the survey are discussed in sections of the paper that follow.

## "Cultural Data Strategy for Artificial Intelligence"

The "Cultural Data Strategy for Artificial Intelligence" consisted of a two-day structured roundtable discussion amongst approximately forty invited participants representing various cultural sector organizations in Canada including Canadian film, television, music, broadcast, and publishing industries, as well as a few non-profit sector representatives and scholars (including the author). The event was convened by Heritage Canada in partnership with Mila, Quebec's Artificial Intelligence Institute (Ministry of Canadian Heritage, 2024) in October 2024.

The broad aim of the roundtable was articulated by the Minister of Canadian Heritage, Hon. St-Onge: "Canada's authors, musicians and artists have important questions about the impact artificial intelligence will have on their work, and we are ensuring that they are part of the conversation as the government develops Canada's cultural data strategy for AI" (Ministry of Canadian Heritage, 2024). Over one-and-a-half days, discussions were structured around three main predetermined AI-related topics: transparency measures concerning the use of cultural content; data licensing of cultural content; and data curation of open source and public domain cultural data" (Ministry of Canadian Heritage, 2024).

Key take-aways from the discussions were published in a "What We Heard" report (Mila, 2024). The report was not intended to be prescriptive or result in specific policy recommendations but rather was an opportunity to explore the perspectives of select cultural sector representatives regarding AI and its implications on creatives and their work (Mila, 2024 at 3). Participants emphasized a need for binding legal instruments and mechanisms to require transparent labeling and disclosure of AI outputs and training data inputs (Mila, 2024 at 4). In
addition, effective consent was a central concern for the participants who overwhelmingly
preferred voluntary direct and/or collective licenses over a compulsory licensing scheme (Mila,
2024 at 5). On the issue of data curation of open source and public domain content, participants
had relatively little to say. For example, a couple of participants expressed concern that open-
source platforms constituting public domain cultural content might compete with proprietary/in-
copyright content platforms (Mila, 2024 at 6). The subdued (and somewhat odd) reaction to data
curation of public domain materials may suggest the limits of the participant sample: most
workshop participants were from established private content industries rather than public interest
organizations such as galleries, libraries, archives, and museums.

In conclusion, copyright law and policy in Canada are still in the early stages of adapting
to the sociotechnical changes wrought by artificial intelligence. Agencies across the federal
government are slowly gathering evidence and insights from a range of consultations and
exploratory stakeholder sessions. Examples of recent or ongoing formal participatory
policymaking processes just discussed suggest that public solicitation of stakeholder perspectives
play a central role although it is unclear how those perspectives inform, or might inform, actual
policy interventions at this stage, particularly where there is some evidence of selection bias and
spotty representation.

Jurisprudential Considerations

Canadian courts have yet to rule on a case involving copyright and GenAI. The gap in
jurisprudential guidance contributes to the state of uncertainty in Canada. A growing body of
legal scholarship engages with the copyright implications of AI but, on the whole, these
contributions lack consensus. Legitimate interpretations and arguments range from a total “red
light” ban (unauthorized uses of copyright protected content by GenAI systems constitute
copyright infringement), to unfettered “green light” permission (unauthorized uses of copyright
protected content by GenAI systems are non-infringing fair dealings (Canada) or fair uses
(U.S.)), to a range of “yellow lights” cautions (some authorized uses of copyright-protected
content in certain instances will be non-infringing while other uses are infringing) (see e.g.
Centivany, 2024; Craig, (2021)).

Cases are being litigated, however, in the United States and other comparable
jurisdictions. While such rulings will not bind judicial or legislative sensemaking or decision-
making in Canada, they are likely to bear on pragmatic issues and collective consensus,
particularly because many of the key AI companies are headquartered in the United States and
because copyright law in Canada shares similarities with U.S. law through its common ancestry.
For example, the “fair use” exception in the U.S. – which most copyright scholars agree will be
central to courts’ decisions on AI -- bears some relationship to Canada’s “fair dealing”
provisions.

Open Questions: Findings and Discussion
Findings from this research are organized around six themes foregrounded in the federal consultation and roundtable workshop previously discussed. These themes include: (1) the use of copyrighted works in AI training models, (2) authorship and ownership of AI-generated content, (3) liability stemming from GenAI outputs, (4) transparency measures concerning the use of protected content, (5) licensing issues related to protected content, (6) the curation of public domain cultural datasets. This section clarifies what we know and what remains to be determined with respect to each of these areas, thereby highlighting key issues for future copyright law and policy consideration.

## Use of Copyrighted Works in AI Training Models

The Canadian government's interest in the use of copyrighted works to train AI models focuses primarily on text and data mining (“TDM”) activities. The legal status of TDM has not yet been determined under existing copyright law. Several considerations are relevant to the question of whether it is or ought to be permissible to use copyright protected works in AI training models.

First, TDM is not a monolithic category but rather consists of several *distinct activities* each of which may give rise to different degrees of liability. For example, creating an AI training model may involve creating tools to scrape data, marketing or selling the tools to others, using the tools to scrape and copy protected content, and storing scraped content in one or more data repositories or databases. The creation of AI training models involves a chain of linked activity and interaction, and each link may or may not give rise to liability for copyright infringement. A more granular and nuanced understanding of what activities and interactions constitute TDM will be a necessary precursor to developing clarity and policy guidance on TDM.

Second, TDM often involves *distinct actors* that may play a role in one part of the TDM chain but not other parts. An actor might, for example, create an application that scrapes and compiles data, or create a model that trains on that data, or create a user interface that enables third-parties to query the dataset, or they might utilize the interface to generate new expressive works or new insights from existing works. TDM often involves collaboration or cooperation amongst different actors. It is unclear under existing law where liability attaches and whether liability would transfer amongst actors. Additional clarity around issues of privity and vicarious or contributory liability is essential.

Third, there are currently two streams of analysis of TDM under Canadian copyright law. The first approach argues that TDM falls outside the purview of copyright because copyright only protects expression and TDM only concerns facts and ideas (Craig et al, 2021). In my view, this argument may be compelling where TDM is used for non-consumptive non-expressive uses (such as facilitating search and discovery over a large corpus to facilitate the derivation of new insights about the corpus, i.e. digital humanities research) but it fails to be compelling when TDM is used to generative expressive works that function in the world, and are consumed by humans, much like the original works. Although it may be technically possible to reduce all “works” into a collection of ever-diminishing constituent parts or “data”, such reductionist logics seem disconnected from the reality of expressive works, a point so central to these sorts of determinations that the U.S. Supreme Court, in its 1841 decision that originated fair use,
determined that “the facile use of scissors; or extracts of the essential parts” was not sufficient to
justify the unauthorized use of another’s work (Folsom v. Marsh, 1841). It is not the
disintegration of a work that is key, but rather the addition of human judgment and intellectual
labour to the original that determines whether a secondary use is fair.

The second stream of TDM analysis turns on a fair dealing analysis which, in itself,
consists of two determinations. The first question is whether the use (or dealing) falls under one
of the enumerated permissible categories: “research, private study, education, parody or satire,
criticism or review, or news reporting”. If the use meets that initial criteria, the court will then
determine whether it was “fair”, a determination that requires application of a six-factor test
including: the purpose of the dealing, the character of the dealing, the amount of the dealing,
alternatives to the dealing, the nature of the work, and the effect of the dealing on the work (CCH
v. LSUC 2004, para 53). Under this approach, each work that is ingesting into an AI training
model would require its own (laborious) analysis which, in application, would be untenable.

Finally, the Canadian government is interested in how other countries regulate TDM and
might look to other jurisdictions for guidance. A simple review shows the purpose of the use
tends to be the critical factor. For example, Japan permits TDM for data analysis, the UK and
France allow TDM for non-commercial research purposes, the European Union permits TDM for
scientific research by research organizations, Singapore allows TDM for computational data
analysis, and the United States permits “transformative uses” of TDM that add “something new,
with a further purpose or different character, altering the first with new expression, meaning, or
message” (Campbell v. Acuff-Rose, 1994 at 579; Leval, 1990 at 1111).

Taken together, and in light of the technical features of TDM and the emerging legal and
policy consensus, we can hypothesize that TDM may be permissible in Canada where the uses of
it are fair and *socially productive*. Social productive uses would include research, education,
criticism, parody, non-consumptive and/or non-expressive uses, and uses that result in secondary
works that serve a new and different function or purposes that the original one and are not a
substitute for the original. Where TDM is used to train GenAI models for those purposes, a court
may be more inclined to accept the work-to-data disintegration arguments mentioned above
(absolving it of conducting a work-by-work analysis under the second fair dealing prong) and
hold the use permissible. Where TDM is used for other purposes, for example, to train a GenAI
model that serves a non-educational, commercial purpose, the use of protected works in TDM
may be rejected. In addition, legal and policy reforms must clarify aspects of contributory
liability (or exculpation) related to links in the TDM chain.

## Authorship and Ownership of AI-Generated Content

Under Canadian law, works require some significant degree of human authorship. GenAI-
authored works will not meet the Act's authorship requirements for several reasons. First,
authorship requires “skill and judgment” which refers to the use of one’s “knowledge, developed
aptitude or practiced ability in producing the work” and one’s “capacity for discernment or
ability to form an opinion or evaluating by comparing different possible options in producing the
work” (CCH Canadian Ltd v. Law Society of Upper Canada, 2013). Furthermore, copyright
rights require a degree of originality which itself requires an “exercise of intellectual effort” in
the “expression of ideas” (CCH Canadian Ltd v. Law Society of Upper Canada, 2013). The
government should clarify that an “author” must be human under the Act.

Questions might arise related to whether the author of an LLM (computer code is
copyrightable) could be considered the author of its outputs. I argue that the nexus between the
author of the AI and its outputs are too tenuous to justify copyright protection by proxy. The
programmer does not meet just-stated requirement of authorship under the Act with respect to
the AI outputs. In addition, given that copyright rights are meant to incentivize creation, no such
incentive is needed where an output is machine-generated.

There may be instances where a human-AI interaction results in expressive works.
Whether the products of a human's use of GenAI tools may create a copyrightable work requires
a fact-based case-by-case determination delineating where protectable human-authorship ends
and unprotectable machine-authorship begins. Given that a primary objective of the Act is to
incentive authors to create, it is worth considering whether the limited monopoly afforded by
copyright is the incentivizing factor for AI-assisted works, or whether some non-copyright-
related interest such as the ease of production, low cost, and so forth may be incentive enough. I
argue that, expect for instances where AI tools were used for late-stage refinements of a
substantially human-created work (e.g. proofreading), copyright-related incentives are not
necessary for human-AI works and therefore they should enter the public domain.

## Liability Stemming from GenAI Outputs

In addition to liability stemming from GenAI inputs (as just discussed with respect to TDM),
GenAI outputs may also constitute copyright infringement. The outputs of GenAI systems may
be infringing where, absent some countervailing protection like fair dealing, they constitute
unauthorized derivative works or are substantially similar to existing protected works and there
is evidence that the AI model trained on or accessed those existing works. Whether a work was
included in the AI training set will be a critical evidentiary factor and one that will prove difficult
to establish since, as the next section discusses, few AI training datasets are currently,
transparently disclosed.

Furthermore, a finding of infringement under Canadian copyright law requires causation
(that one of the exclusive rights was unlawfully impinged) and some degree of responsibility for,
or control over, the unlawful act. Applied to GenAI systems, a question arises as to who is
responsible for, or has sufficient control over, infringing outputs. We can envision situations
where the developer of the AI model may be held vicariously liable for the infringing outputs of
the model, even when those outputs result from user prompts that the developer has no direct
control over, because they knew or should have known that the models could be used to produce
such outputs (particularly where relevant protected works were used in the training set).
Likewise, we might envision some culpability for users who set out to generate outputs that are
substantially similar or identical to protected works.

The government should expectations and reporting requirements, if any, regarding
transparent disclosure of works included in GenAI training sets. It should also clarify how the
existing test for liability applies to the creators and users of GenAI systems, bearing in mind
 direct and secondary liability, and make revisions as it sees fit to provide certainty and to balance
 the interests of creators and the public.

Transparency Measures Concerning the Use of Cultural Content

Most of the dominant GenAI systems we have seen so far are black boxes from the perspective
of rightsholders and the public. Rightsholders, policymakers, and technology ethicists have
expressed concern around the lack of transparency and explainability of GenAI systems.
Participants in the roundtable event discussed previously emphasized the importance of informed
consent, mandatory disclosure requirements, opt-in licensing, and technical mechanisms to
enable tracking of works through the input-training-output cycle. Transparency was also deemed
critical with respect to determinations of liability as just discussed. Some roundtable participants
highlighted the need for independent watchdog groups to oversee compliance and the
development of technical standards regarding metadata throughout the AI lifecycle (Mila, 2024
at 3). As GenAI systems continue to proliferate, and their impacts become more visible, we
should anticipate a growing need for the government to provide legislative guidance on AI
transparency.

Current policy discussions can veer toward hopelessness and futility regarding
transparency interventions. Many existing datasets consist of vast amounts of content scraped
from the Internet such that retroactively determining rightsholder interests, labeling with
metadata, and tracing works, as well as providing rightsholders with a meaningful opportunity to
opt-out, may be functionally impossible. But that, in itself, may be a useful finding because it
suggests that we need to consider the purpose of transparency, not simply its reality as a
technical matter.

Too often transparency is treated as an end rather than a means to an end. It is important
to clearly define the purpose of transparency measures and ensure that they are linked to
complementary mechanisms for achieving their intended goals. Where mechanisms are not
feasible, we can look for alternate ways to reach the desired goal. Potential objectives could
include safeguarding against particular harms, addressing biases in the models, securing
equitable compensation, or obtaining meaningful, ongoing consent. Furthermore, being
transparent is not fully exculpatory. Being transparent about “bad” acts does not alter the fact
that the acts are “bad”.

Data Licensing Issues Related to Cultural Content

Participants in the roundtable event overwhelmingly favored licenses for the use of
content in AI training models. Participants preferred voluntary direct licenses with individual
creators and collective licensing agreements and strongly opposed compulsory licenses that
would obviate rightsholder consent. Even when a rightsholder gives consent for a work to be
included in a dataset, it is very difficult, as a technical matter, to determine how to weigh and
remunerate a particular contribution with respect to a given output (which might result from
hundreds or thousands of inputs).
Cultural sector stakeholders' preferences notwithstanding, a coalition of Canadian law scholars submitted comments to a federal consultation that recommended against requiring licenses for TDM activities. They argued that such a requirement would be a barrier to participation in TDM activities, would exacerbate problems already present in publishing and academic research (such as high subscription fees for access to scholarly communications and other barriers to conducting research), and would undermine fair dealing by overlaying user's rights with a compensatory scheme (Craig et al, 2021).

## Data Curation of Open Source and Public Domain Cultural Data

There is little in the way of legitimate copyright-based objections to GenAI trained on public domain content which, by definition, is free to use by all for any purposes. Some beneficiaries of established cultural content industries expressed concern during the roundtable that open source and public domain-based AI may compete with their proprietary content, but that is not a harm copyright is intended to ameliorate. Rather, the aim of copyright law in Canada is to promote the creation and sharing of cultural works for the benefit of all and ensure that adequate incentives exist to foster creativity.

To the extent that cultural heritage content consists of works of marginalized or vulnerable communities, or groups who have been historically underserved and oppressed by dominant legal regimes, inclusion of those works might further exacerbate existing harms and inequities despite being lawful under the Copyright Act. For this reason, the government should ensure that consultations and rulemaking procedures in the future are inclusive of a wide range of stakeholders and a thoughtful approach that mitigates harm and protects creators' livelihoods, while balancing communities' and society's interests in preserving and supporting access to our shared cultural heritage.

## Conclusion

This research provides a window into contemporary Canadian copyright law and policy efforts with respect to AI. Emerging technologies challenge existing regimes and GenAI is no exception. Returning to the overarching purposes of copyright law is always important, but it is especially so during periods of sociotechnical transformation and transition. Emerging technologies like GenAI challenge existing norms and rules, and may create or make visible policy vacuums, disequilibria, and instability that zigzag across policy domains. Whether or not copyright law is the right tool will depend upon whether the interests sought to promote, and harms sought to ameliorate, fall under the purview of the Act. By highlighting and discussing the most important open questions concerning AI, : (1) the use of copyrighted works in AI training models, (2) authorship and ownership of AI-generated content, (3) liability stemming from GenAI outputs, (4) transparency measures concerning the use of protected content, (5) licensing issues related to protected content, (6) the curation of public domain cultural datasets, this research highlights the areas of import for future copyright law and policy consideration.
Recent transitions in federal leadership have hampered formal policymaking processes. This slower pace provides an opportunity for thoughtful reflection and deliberation on policy processes, participation, and desirable outcomes. Several important consultation efforts are ongoing including the “Consultation on Copyright in the Age of Generative Artificial Intelligence” and the “Cultural Data Strategy for Artificial Intelligence” initiative. The policy pause enables us to appreciate where our existing efforts have fallen short and ask whether greater participation may be called for. It also allows us to witness what other jurisdictions have done and with what outcomes.

By analyzing GenAI in light of participant perspectives and evolving doctrinal and policy approaches at home and in other jurisdictions, we can recognize where Canadian governmental intervention will be most needed. Guidance and clarification are particularly critical with respect to the copyright implications of TDM, AI training model inputs and transparency including the potential for metadata and tracing, the status of GenAI outputs, appropriate uses and limits of licenses, and how we might ethically approach the curation of public domain content and open-source datasets.

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https://www.canada.ca/en/canadian-heritage/news/2024/09/government-of-canada-partners-with-mila-to-guide-the-development-of-a-cultural-data-strategy-for-artificial-intelligence.html