Canada

In Canada, legislative powers are divided between the federal government and the provinces pursuant to sections 91–95 of the Constitution Act, 1867 (www.laws-lois.justice.gc.ca/eng/const/page-1.html). Legislation that may impact the development and use of AI can be adopted by both provincial legislatures (e.g., under their authority over property and civil rights or local matters) and the federal government (e.g., under its powers over trade and commerce or criminal law). In practice, effective policymaking, especially in complex and rapidly evolving areas like AI, often requires joint or coordinated action between these levels of government.

Legislative powers in Canada are also constrained by the Canadian Charter of Rights and Freedoms (Charter) (www.publications.gc.ca/collections/Collection/CH37-4-3-2002E.pdf). The Charter is entrenched in the Constitution, and any law that is inconsistent with its provisions is, to the extent of the inconsistency, of no force or effect. The Charter is likely to play a prominent role in ensuring that AI, and particularly government-controlled AI, is developed in a manner that respects fundamental human rights.

While the Charter applies to state actors only, human rights legislations enacted by the federal and the provincial legislatures may be applicable to the private sector and individuals. The scope of the rights provided by those legislations as well as the modalities of their application vary significantly from one jurisdiction to another.

Several provisions of the Charter may limit how AI is developed or used:

• Section 7 of the Charter asserts the right of everyone to not be deprived of “life, liberty and security of the person” except in accordance with the principles of fundamental justice. The right to life is self-explanatory. The right to liberty is at stake when a law imposes the penalty of imprisonment or where an inmate suffers a change in conditions of confinement or is denied reclassification (Dumas v. Leclerc Institute [1986] 2 S.C.R. 459). The right to security of the person has been interpreted to mean health, safety, and personal autonomy ( v. Morgentaler (No. 2) [1988] 1 S.C.R. 30; Carter v. Canada 2015 SCC 5). Applications of AI that could offend section 7 of the Charter include, for instance, using AI in a discriminatory manner to evaluate priority in access to life-saving care or to assess recidivism in prisoners.
• Section 8 of the Charter asserts the right to be “secure against unreasonable search or seizure”, which limits the techniques available to the police to look for and obtain evidence of wrongdoing. The protection against unreasonable search or seizure has been interpreted as providing a right to privacy more broadly even outside the criminal context (Hunter v. Southam [1984] 2 S.C.R. 145). For instance, the use of AI by authorities for facial recognition in public spaces may raise Charter-compliance concerns. Personal information has been held to include electronic information such as the names and addresses associated with ISP subscriber information ( v. Spencer 2014 SCC 43) and IP addresses (R. v. Bykovets 2024 SCC 6).
• Section 9 of the Charter asserts the right not to be “arbitrarily detained or imprisoned”, including by police for the purpose of investigation. The right not to be arbitrarily detained places limits on investigative detention by the police. For instance, “the presence of an individual in a so-called high crime area is relevant only so far as it reflects his or her proximity to a particular crime” ( v. Mann 2004 SCC 52). Police departments that use AI systems that involve profiling or facial-recognition technologies, for example, will have to meet the burden of showing reasonable grounds to suspect an individual is connected to a particular crime. Moreover, biased data that leads to inadvertent racial profiling may result in unconstitutional detention, since the Supreme Court of Canada has held that using racial profiling in detaining an individual will be relevant in determining whether detention is arbitrary (R. v. Le 2019 SCC 34).
• Section 15 of the Charter asserts the right to equality before the law and to equal protection and equal benefit without discrimination. It requires substantive and not merely formal equality. Therefore, it protects from direct or intentional discrimination, but also from adverse impact discrimination (Andrews v. Law Society of B.C. [1989] 1 S.C.R. 143). Given the breadth of the above-referenced Charter protections, AI systems that involve profiling or discriminatory outcomes based on any of the Charter-protected attributes, or that impede upon any of the other referenced fundamental rights, will be subject to particular scrutiny.

Some public organizations have adopted internal policies promoting Charter compliance. For instance, the Toronto Police Service Board has adopted a policy entitled, Use of Artificial Intelligence Technology, which seeks to protect human rights and prevent bias against vulnerable groups (see www.tpsb.ca/use-of-artificial-intelligence-technology).

The Charter protections are complemented by statutory protections. For instance, in Ewert v. Canada 2018 SCC 30, the Supreme Court of Canada rejected the argument raised by an Indigenous individual that the use of algorithmic risk assessment by Canadian correctional services in making decisions regarding prison conditions, access to services and parole violated his sections 7 and 15 rights, but ruled, however, that such use breached the correctional services’ statutory duty to ensure that the tools they relied on when making such decisions were as accurate as possible.

Moreover, rights to procedural fairness applicable to administrative and judicial processes may provide additional protections. Failure to disclose to inmates relevant information about a computerized tool used for security reclassification decisions has been found to unlawfully deprive them of their residual liberty (May v. Ferndale Institution 2005 SCC 82). Courts may also evaluate the use of AI systems in decision-making processes, for instance in the immigration context (Haghshenas v. Canada (Citizenship and Immigration) 2023 FC 464).

As in other jurisdictions, Canada’s Patent Act only permits the patenting of certain subject matters. Most relevant to AI, a “mere scientific principle[s] or abstract theorem[s]” cannot be patented (see section 27(8) at www.canlii.ca/t/7vkn#sec27).

The Canadian Intellectual Property Office (CIPO) developed specific practices to assess “computer-implemented inventions” as set out in its Manual of Patent Office Practice (Manual) (see www.publications.gc.ca/site/eng/9.836893/publication.html) and supplemented by a guidance document in November 2020 (see www.ised-isde.canada.ca/site/canadian-intellectual-property-office/en/patentable-subject-matter-under-patent-act). In the Manual, CIPO states that computer programs, data structures and computer-generated signals are not, on their own, patentable subject matter. Its 2020 Guidance Document further explains that a scientific principle or abstract theorem, including AI systems, is only patentable when part of a combination that “has physical existence or manifests a discernible physical effect or change and relates to the manual or productive arts.” The mere use of a computer does not suffice.

CIPO recently concluded consultations on proposed updates to the Manual that include explicit guidance on the use of AI in computer‑implemented inventions; while the updates have not been finalized, these proposed changes suggest that CIPO will treat machine‑learning and AI algorithms in the same manner as other computer algorithms, requiring some element of physicality or a demonstrable improvement to computer functionality (see also Stanley Victor Campbell (Re) 2024 CACP 18, discussed below).

On the question of whether an autonomous AI system could be an inventor, the Patent Act does not define the term “inventor” and, unlike in the United States, does not refer to “individuals” in defining an “invention”. The Federal Court of Canada has interpreted an “inventor” as being a “natural person,” but the question of whether an autonomous AI system could be considered an inventor has not yet been squarely put before the court. However, an appeal on this specific issue has been made to the Federal Court and will likely be heard in 2026 (Court file No. T-4928-25, appealing Thaler (Re) 2025 CACP 8, discussed below).

In Canada, the Copyright Act governs copyright and protects key components of AI technologies including software (source and object code) and databases.

Under current jurisprudence interpreting the Copyright Act, it is unlikely that an AI system would be considered an “author” of works it generates. Copyright case law suggests “authorship” must be attributed to a natural person, as reflected by connecting the term of protection to the human author’s lifespan and the concept of “moral rights” presupposing a human author with certain inalienable interests connecting to that person’s honour or reputation. Although CIPO has registered AI-generated works, this is far from conclusive — unlike other intellectual property offices, CIPO does not substantively assess the claims made in an application for copyright registration.

The Government of Canada has not yet updated the Copyright Act to address AI specifically, but it has launched consultations and published papers to shed light on how the challenges created by the intersection of copyright and AI might be addressed in future amendments to the act. The most recent consultation on Copyright in the Age of Generative Artificial Intelligence concluded in January 2024 (see www.ised-isde.canada.ca/site/strategic-policy-sector/en/marketplace-framework-policy/consultation-paper-consultation-copyright-age-generative-artificial-intelligence#s22) and was followed by a report summarizing the consultation responses (www.ised-isde.canada.ca/site/strategic-policy-sector/en/marketplace-framework-policy/consultation-copyright-age-generative-artificial-intelligence-what-we-heard-report).

The Copyright in the Age of Generative Artificial Intelligence Consultation Paper raises three possible approaches to address authorship and ownership of AI-generated works:

• make AI-generated work without a minimum creative contribution from a human author ineligible for copyright;
• attribute authorship to the human(s) who arranged for the AI-generated work to be created, based on factors to distinguish AI-assisted works that meet the human authorship threshold from those that do not; or
• create a new, unique set of rights for AI-generated “authorless” works that would grant economic rights to persons like the AI developer, deployer, or user even where they did not provide any original contribution to the works but without deeming them an author.

The Consultation Paper also deals with:

• text and data mining copyright-protected works and the complexity of obtaining a vast number of authorizations for data used to train AI systems. There is at least the possibility of a targeted exception for copying for the purpose of informational analysis; and
• infringement and liability issues surrounding who might be liable for infringing AI technology. Culpability for copying becomes less clear as human involvement decreases.

(See www.ised-isde.canada.ca/site/strategic-policy-sector/en/marketplace-framework-policy/consultation-paper-consultation-copyright-age-generative-artificial-intelligence#s22).

The issue of training AI with copyright-protected works is also set to be addressed by the Ontario Superior Court in Toronto Star Newspapers Limited v. OpenAI Inc. (see more below).

In Canada, trade secret law is based on the common law of confidential information (except in Quebec, where civil law principles apply). In Canada, algorithms, including AI systems, can be protected as confidential information, including formulae, compilations of information, techniques, processes, and patterns, often through confidentiality agreements and control measures. No registration is required, and confidential information law does not require specific subject matter, unlike patent law. In Canada, obtaining or communicating a trade secret by fraudulent means is a crime under section 391(1) of the Criminal Code.

Patent law

The Federal Court of Appeal referred to the patentability of AI in obiter in Canada (AG) v. Benjamin Moore & Co. 2023 FCA 168. The decision dealt with the patentability of computer-implemented inventions more generally, and the FCA cautioned the analysis is “highly fact specific,” especially as technology is becoming more and more complex with “the advent of artificial intelligence.” AI patentability will be assessed case-by-case for now.

Recent decisions from the Commissioner of Patents have addressed AI more directly. In Stanley Victor Campbell (Re) 2024 CACP 18, the Commissioner considered whether an AI-assisted medical claims adjudication system was patentable. The Commissioner held that the “mere inclusion of AI and neural network terminology does not elevate the system beyond a generic data processing tool” (paragraph 49). The decision applies existing computer-implemented invention principles to AI systems, consistent with CIPO’s draft updates to the Manual.

In Thaler (Re) 2025 CACP 8, the Commissioner determined that an AI system cannot be an inventor under the Patent Act. Relying on statutory context and jurisprudence, the decision concluded that an “inventor” must be a natural person capable of holding and assigning legal rights. This case is currently being appealed to the Federal Court and will likely be heard in late 2026/early 2027 (Court File No. T-4928-25).

Copyright law

The Alberta Court of King’s Bench held in Geophysical Service Inc. v. Encana Corp. 2016 ABQB 230 that “a human author is required to create an original work for copyright purposes” but accepted that human intelligence can be significantly assisted by computer systems. A modicum of “skill and judgment” in assembling the work with the help of technology is required. In Stross v. Trend Hunter Inc. 2020 FC 201, the Federal Court considered whether an AI technology generating consumer trend insights for its clients was eligible for the “fair dealing” defence to an infringement claim. The court found that the technology was a computerized form of market research that passed the first “allowable purposes” stage of the fair dealing analysis. However, the technology failed the second “fairness” stage of the fair dealing analysis because it was used for commercial purposes rather than a broader public interest purpose, and because the defendant did not follow its own copyright policies to screen out images with a copyright notice or a watermark.

The Ontario Superior Court in Toronto Star Newspapers Limited v. OpenAI Inc. 2025 ONSC 6217, considered a preliminary motion in an action alleging that OpenAI scraped and copied Canadian news content to train and operate its GPT models without authorization. The defendants argued that Ontario lacked jurisdiction and sought to set aside service and stay the proceeding in favour of the United States. The court declined to oust its jurisdiction, finding sufficient connecting factors to Ontario, including alleged reproduction of the plaintiffs’ works in Canada and the role of Ontario‑based website terms of use.

Samuelson‑Glushko Canadian Internet Policy and Public Interest Clinic v. Sahni, Court File No. T‑1717‑24, raises the question of whether AI can be considered an “author” under the Copyright Act, and whether works created using AI tools possess the requisite level of “skill and judgment” to attract copyright protection. The applicant (CIPPIC) seeks to expunge or amend a registration for an artwork titled Suryast, which lists both a human creator and the “RAGHAV Artificial Intelligence Painting App” as co‑authors. In its application, CIPPIC argues that the work’s AI‑generated contributions do not satisfy the statutory originality threshold, and that authorship must be limited to natural persons.

Importantly, CIPO does not substantively examine copyright registration applications; it merely reviews submissions for compliance with the registration requirements of the Copyright Act and Regulations. As a result, CIPO’s acceptance of the underlying application does not imply that the Office intended to recognize AI as an author or confer authorship rights on an AI system. The application has yet to be heard.

The federal government has in recent years put data governance at the forefront with the publication in 2019 of Canada’s Digital Charter. Its stated goal is to make Canada a “competitive, data-driven, digital economy,” notably to favor AI innovation.

The applicable data protection laws for the private sector are the Personal Information Protection and Electronics Documents Act (PIPEDA) at the federal level and the substantially similar provincial statutes:

• Alberta’s Personal Information Protection Act, SA 2003, c P-6.5;
• British Columbia’s Personal Information Protection Act, SBC 2003, c 63; and
• Quebec’s Act Respecting the Protection of Personal Information in the Private Sector, c P-39.1 (Quebec Act).

There are also a variety of provincial statutes that apply to personal health information.

In September 2022, the Province of Quebec enacted Law 25, which amended the Quebec Act to bring it closer to the EU GDPR model. The Quebec Act is the first Canadian law to regulate automated decision-making, imposing transparency and explainability requirements in relation to automated decision-making involving personal information (see section 12.1 of the Quebec Act). The federal privacy framework is also now in transition following the introduction of Bill C‑36 on June 15, 2026 (www.parl.ca/Content/Bills/451/Government/C-36/C-36_1/C-36_1.PDF). Bill C‑36, which would enact the Protecting Privacy and Consumer Data Act (PPCDA), representing a third attempt in six years to modernize Canada’s private-sector privacy regime. This bill proposes to enhance the privacy rights of individuals and to generally modernize federal privacy law to align it closer to international standards such as the GDPR. Its provisions provide for broader exceptions to consent, regulation of automated decision-making systems, significant administrative monetary penalties and a new oversight framework centred on the proposed Digital Safety and Data Protection Commission of Canada.

Recent enforcement actions, such as the joint investigation by the Canadian privacy commissioners into TikTok’s handling of personal information (www.priv.gc.ca/en/opc-actions-and-decisions/investigations/investigations-into-businesses/2025/pipeda-2025-003/) underscore the high bar for transparency and meaningful consent, particularly for minors. This signals that organizations deploying AI systems with significant personal information components should expect strict scrutiny and robust compliance requirements around consent, explainability, and accountability.

Organizations operating in regulated industries must also comply with sector‑specific frameworks, including requirements issued by the Office of the Superintendent of Financial Institutions (OSFI), Canada’s prudential regulator for federally regulated financial institutions, and the Canadian Investment Regulatory Organization (CIRO), the national self‑regulatory body overseeing investment and mutual fund dealers. These frameworks impose additional obligations relating to governance, cybersecurity, operational risk, and supervisory controls, including for AI and machine learning models (www.osfi-bsif.gc.ca/en/guidance/guidance-library/guideline-e-23-model-risk-management-2027).

Canada has introduced a wide variety of open data initiatives to better streamline secured sharing of personal information. At the provincial level, for instance, Quebec enacted the Act respecting health and social services information, which allows for the sharing of a patient’s medical information for the purposes of research.

PIPEDA governs the disclosure of personal information by private businesses and specifically carves out an exception for the disclosure and use of personal information without the need for proper knowledge and consent, if it is for the purpose of statistical, scholarly study or research (see subsections 5(2)(c) and 5(3)(f)). However, under the Accountability Principle of PIPEDA, private businesses that share the personal information in their possession or custody remain responsible for that information even if that personal information is transferred to a third party for processing. Therefore, private businesses should ensure that they put in place contractual or other safeguards to ensure personal information is properly protected and handled once transferred. Similar requirements apply pursuant to provincial privacy legislation. In addition, the Quebec Act imposes a requirement to conduct privacy impact assessments in respect of cross-border data transfers.

The federal budget tabled in the fall of 2025 signalled a renewed commitment to modernizing Canada’s privacy framework, including a potential update to PIPEDA to incorporate a data mobility framework (www.budget.canada.ca/2025/report-rapport/anx5-en.html). A data mobility framework would allow individuals to securely transfer their personal information between organizations, enhancing consumer choice and fostering competition in digital markets. At the same time, the budget also alluded to a broader, economy-wide approach to data sharing, which could signal a re-examination of consent-based requirements under PIPEDA, including the possible introduction of additional lawful bases for processing personal information and provisions applying specifically to AI systems in a targeted manner. The 2025 fall federal budget also signalled the government’s intention to prioritize adoption of federal open banking legislation, under the draft Consumer-Driven Banking Act (www.lois.justice.gc.ca/eng/acts/C-36.75/page-1.html).

The direct regulation of AI in the context of facial recognition and voice data is limited in Canada. Quebec’s Act to Establish a Legal Framework for Information Technology requires organizations to disclose to the Commission d’accès à l’information (CAI) the existence of biometric databases and biometric identification or verification technologies that it intends to use prior to its entry into service.

Privacy laws also play a role in regulating such technology. PIPEDA and the provincial private sector privacy laws regulate the use of AI in facial recognition and voice data technologies through their consent requirements. The Quebec Act explicitly requires express consent for the collection, use and disclosure of biometric data.

Several decisions shine light on how the consent principle has been applied to regulate use cases of biometrics and AI.

• In a 2021 decision, the privacy commissioners found that Clearview AI was maintaining a database of billions of photos of individuals taken from the internet without consent to train its facial recognition technology. The commissioners found that Clearview AI had violated federal and provincial privacy laws even if it was no longer offering its technology in Canada (priv.gc.ca/en/opc-actions-and-decisions/investigations/investigations-into-businesses/2021/pipeda-2021-001).
• In March 2022, the OPC reported on its investigation into Rogers Communications Inc.’s use of a VoiceID program. The OPC’s report found that, while the use of voice biometrics for authenticating account holders is an effective solution for addressing the legitimate need for account authentication and security, Rogers’ VoiceID program was not compliant with the consent obligations under PIPEDA. Among other requirements, PIPEDA mandates obtaining an individual’s express consent before creating a voiceprint (priv.gc.ca/en/opc-actions-and-decisions/investigations/investigations-into-businesses/2022/pipeda-2022-003).

In addition to these federal and multi‑jurisdictional investigations, the CAI has recently issued decisions reinforcing Quebec’s strict consent requirements in the biometric context.

• In February 2025, the CAI released a decision prohibiting Metro Inc. from deploying a facial‑recognition pilot intended to identify individuals previously involved in shoplifting or fraud. The CAI found that the project constituted identity verification, which under sections 44–45 of the Act to Establish a Legal Framework for Information Technology requires express consent. Operating the system without such consent amounted to a significant invasion of privacy.
• In May 2025, the CAI found that an employer’s enhanced video‑surveillance system unlawfully infringed employees’ privacy because workers were not adequately informed, and the scope of monitoring exceeded what was necessary and proportionate. The decision underscores that surveillance systems — especially those capable of high‑resolution or analytic functions — must meet strict transparency and consent‑related obligations.

In August 2025, the OPC published its Guidance for processing biometrics for businesses, which clarifies how PIPEDA’s existing framework applies to biometric information. The guidance emphasizes the importance of express consent before creating or using a biometric identifier, strict purpose limitation, data minimization, heightened security safeguards, and clear retention and deletion practices. It recommends conducting formal risk assessments before deploying biometric systems. These expectations underscore that organizations must adopt robust governance and accountability measures when handling biometric data, in addition to complying with federal and provincial legal requirements.

As referenced in Section 1, above, the Charter affords Canadians certain protections against human rights violations committed by governments in relation to governmental activities. However, the Charter does not guarantee equality or protection against discrimination by non-governmental entities. The protection against discrimination from actions by private actors is instead enshrined in Canada’s federal Human Rights Act, R.S.C. 1985, c. H-6, as well as various provincial and territorial human rights acts and codes.

These human rights laws are similar to section 15 of the Charter and also flow from Canada’s international human rights obligations. These laws prohibit discrimination on specific, enumerated grounds including gender, race, age, religion, disability, and family status. While they vary slightly in their language and scope, each promote the equal treatment of Canadians in employment, commercial, and other contexts.

Canada’s human rights laws recognize that discrimination can take place in various forms, including direct, indirect, constructive, and/or “adverse effect” discrimination. Some statutes, including Manitoba’s Human Rights Code, C.C.S.M, c. H 175, recognize “systemic discrimination.” This concept has been interpreted by the Supreme Court of Canada as referring to “practices or attitudes that have, whether by design or impact, the effect of limiting an individual’s or a group’s right to the opportunities generally available because of attributed rather than actual characteristics” (Moore v. British Columbia (Education) 2012 SCC 61, referencing Canadian National Railway Co. v. Canada (Canadian Human Rights Commission) [1987] 1 S.C.R. 1114).

The Supreme Court of Canada has defined discrimination as the imposition of “differential treatment based on protected grounds, either explicitly or through adverse impact” when such treatment “has the effect of reinforcing, perpetuating, or exacerbating disadvantage” (Fraser v. Canada (Attorney General) 2020 SCC 28). Proof of discriminatory intent is not necessary to conclude that a discriminatory practice has taken place.

AI systems are often built around the use of categorized sets of data. As such, algorithms that classify individuals based on gender, race, age, or any other enumerated human rights grounds pose a greater risk of generating discriminatory outcomes under Canadian law (and triggering a corresponding human rights claim) than systems that avoid such classifications.

The Law Commission of Ontario (LCO) and the Ontario Human Rights Commission have recently created an AI impact assessment tool to help organizations comply with human rights obligations. The LCO has also called for comprehensive AI regulatory reforms to better ensure that AI systems comply with Canada’s human rights laws.

As cyber threats continue to grow in frequency, scope, and sophistication, Canada is moving toward a sweeping new regulatory framework for protecting federally regulated critical infrastructure. To advance this effort, the federal government introduced Bill C‑8 in June 2025: An Act Respecting Cyber Security, Amending the Telecommunications Act and Making Consequential Amendments to other Acts (www.parl.ca/DocumentViewer/en/45-1/bill/C-8/first-reading). Bill C-8 seeks to establish a comprehensive regime to address cybersecurity risks across designated critical infrastructure sectors.

This Bill includes two main parts:

• an amendment to the Telecommunications Act to promote the security of the Canadian telecommunications system; and
• the enactment of the Critical Cyber Systems Protection Act (CCSPA), designed to protect critical cyber services and systems that are vital to national security or public safety or are delivered or operated within the legislative authority of Parliament.

Bill C-8 would amend the Telecommunications Act to enable the federal government to make certain orders concerning telecommunications service providers (TSPs), namely orders that would mandate the:

• use of products and services of specific vendors and other TSPs in telecommunications networks (i.e., regulate the inputs into telecommunications networks); and
• provision of specific telecommunications services in Canada (i.e., regulate the type of telecommunications services offered using telecommunications networks).

Additionally, the proposed amendments to the Telecommunications Act would grant the Minister of Industry the power to direct TSPs to do anything (or refrain from doing anything) that is, in the Minister’s opinion, necessary to secure the Canadian telecommunications system, including the following:

• prohibiting TSPs from entering service agreements for any product or service;
• requiring TSPs to terminate a service agreement;
• prohibiting the upgrade of any specified product or service; and
• subjecting the TSPs’ procurement plans to a review process.

Pursuant to the second part, the CCSPA is designed to enable the federal government to protect systems and services of national importance and establishes a broad regulatory framework enabling the federal government to:

• define and strengthen baseline cybersecurity for systems and services of critical national importance (e.g., telecommunications services, interprovincial or international pipelines and power line systems, nuclear energy systems, transportation systems within the jurisdiction of the federal government, banking systems, clearing and settlement systems, and any other industry regulated by the federal government that may be added to the scope of this Act at a later date);
• require designated operators to develop and implement certain cybersecurity programs;
• ensure that cyber incidents impacting vital systems and services are reported;
• issue binding Cybersecurity Directions; and
• encourage compliance through the introduction of administrative monetary penalties (i.e., maximum penalties of CAD 15 million for designated operators and CAD 1 million for directors and officers).

Bill C-8 has passed the second reading stage in the House of Commons and, at the time of writing, continues its legislative journey towards becoming law.

Thus far, there has been no legislation specifically addressing the intersection of AI and competition law in Canada, nor has there been any enforcement actions or litigation under the Competition Act. However, recent statements from the federal government have confirmed that AI is top of mind for the Competition Bureau (Bureau). Three key issues are outlined below in turn.

Conspiracy

Any agreement between competitors to fix prices, allocate markets or lessen supply contravenes section 45 of the Competition Act and is a criminal offense.

Many have expressed concerns about AI both facilitating collusion between competitors and making it harder for regulators to detect collusion. In July 2025, the Bureau released an “Algorithmic pricing and competition discussion paper” (www.competition-bureau.canada.ca/en/how-we-foster-competition/education-and-outreach/publications/algorithmic-pricing-and-competition-discussion-paper), which among other things posits that algorithmic pricing could facilitate both explicit and tacit agreements to fix prices between competitors. According to the Bureau, competitors could use the same algorithm to process their data that, in turn, sets or recommends prices to each of them to earn the highest combined profit for all. In doing so, these algorithms could form “hub-and-spoke” conspiracies, enabling competitors to implement a coordinated pricing strategy, which would run afoul of section 45. In January 2026, the Bureau published an overview of stakeholder responses to its discussion paper, which confirmed that respondents were similarly concerned that algorithms would make it easier for competitors to reach and sustain collusive agreements (www.competition-bureau.canada.ca/en/how-we-foster-competition/education-and-outreach/publications/consultation-algorithmic-pricing-and-competition-what-we-heard#secD-02-1).

Other forms of collaborations (e.g., joint ventures, strategic alliances) that do not rise to the level of criminal conspiracy but nonetheless rely upon forms of algorithmic coordination or collusion, may be subject to review under the civil provisions at section 90.1 of the Competition Act where anticompetitive effects arise. Algorithmic collusion may also be used to facilitate wage-fixing and no-poach agreements and bid rigging practices, which are criminalized under sections 45(1.1) and 47 of the Competition Act, respectively.

Abuse of dominance

Section 79 of the Competition Act regulates abuse of dominance, which has emerged as an area of focus for the Bureau with respect to AI.

In July 2024, the Bureau made public its investigation into whether a data services provider, Kalibrate, has contravened section 79 of the Competition Act by incentivizing its retail gas customers to reduce price competition through the use of sensitive, non-public competitor data in its algorithm (www.canada.ca/en/competition-bureau/news/2024/07/competition-bureau-advances-an-investigation-into-kalibrates-gas-pricing-services.html). The investigation remains ongoing.

In early 2025, the Bureau announced an investigation into the use of algorithmic pricing in Canadian rental markets, following the US RealPage case. The Bureau subsequently discontinued its investigation into whether suppliers of algorithmic pricing software, namely RealPage and Yardi, had abused a dominant position or entered into anticompetitive collaboration under section 90.1 of the Competition Act (www.competition-bureau.canada.ca/en/how-we-foster-competition/education-and-outreach/competition-bureau-position-statement-regarding-its-civil-investigation-realpages-and-yardis). Ultimately, the Bureau found that: there was insufficient evidence to conclude that RealPage or Yardi occupied a dominant position in a relevant market; and there was a relatively low adoption of revenue management software by Canadian landlords, such that it was unable to demonstrate that competition in Canada was harmed substantially under either the Competition Act’s abuse of dominance or civil collaboration provisions.

In concluding its investigation, the Bureau warned that algorithmic pricing software may distort the competitive process and result in violations of the Competition Act, where it relies on competitively sensitive information; makes it easier to accept and implement pricing recommendations than to reject or override them (e.g., penalizes landlords for rejecting the software’s recommendations); or, artificially inflates or limits the reduction in price recommendations.

Mergers

Under the Competition Act, the Bureau reviews mergers to determine whether they are likely to harm competition in a market in Canada. With the advent of AI technology, the Bureau must now determine how to assess the value of data and AI in merger reviews, such as the impact on economies of scope and scale, how it can be leveraged in vertical and conglomerate mergers, and the added challenges of predicting competitive effects in a constantly evolving space. Thus far, unlike other jurisdictions, the Bureau has not actively reviewed or opposed investments by incumbents into AI start-ups, at least to public knowledge.

Based on past statements, the Bureau generally believes the merger provisions of the Competition Act are sufficiently flexible as to account for mergers in technologically advanced markets. However, it is live to the unique challenges AI poses in merger control. It acknowledged in its 2024 public consultation paper on AI and competition that AI could affect the degree to which market concentration and market power may arise and develop. In its January 2025 consolidated report of responses received, the Bureau highlighted feedback on vertical mergers in particular, with market participants emphasizing the potential for AI to enhance efficiency in such cases but also potentially limiting access to essential inputs and facilitating coordinated effects (www.competition-bureau.canada.ca/en/how-we-foster-competition/education-and-outreach/consultation-artificial-intelligence-and-competition-what-we-heard).

Currently, there is no specific legislation or regulation that addresses AI in the competition context in Canada. The Competition Act relies on general, flexible standards for assessing conduct, rather than industry or subject-specific rules. However, as set out above, the Bureau is closely considering the unique nature of AI markets and the enforcement challenges they pose.

The legal status of AI agents remains unaddressed and “AI agent” as such, as distinct from AI systems, have not been the subject of extensive policy discussions. Currently, AI agents have no independent legal status or personhood in Canada, and they are not recognized as legal entities with rights or duties under current laws. Recent regulatory proposals such as AIDA also treated AI systems as tools subject to human control (and would have imposed oversight obligations). Liability for the actions of AI agents would thus fall on the person or organization deploying or using the agent.

In Moffatt v. Air Canada 2024 BCCRT 149, the British Columbia Civil Resolution Tribunal rejected the claim made by Air Canada that it could not be held liable for false information provided by its website chatbot because this chatbot represented a “separate legal entity that is responsible for its own actions.”

Regulation of AI agent remains in its infancy in Canada and there have been limited sector-specific discussions on the topic. As a notable exception, the issue of agentic AI risks was addressed during a workshop of the Office of the Superintendent of Financial Institutions, the federal banking regulator, held on December 9, 2025 (www.osfi-bsif.gc.ca/en/about-osfi/reports-publications/fifai-ii-collaborative-approach-ai-threats-opportunities-best-practices-workshop-3-ai-financial-0). During this workshop, participants raised the potential systemic risks of AI agents autonomously executing material financial transactions and suggested mitigation measures such as ensuring AI agents are given distinct digital identities and establishing clear guidelines for human oversight.

In general, product liability law applies to manufacturers, producers, and distributors of products that cause harm to consumers. In Canada, product liability law is grounded mainly in the common law principle of negligence (with the exception of the Province of Quebec where civil law rules apply), supplemented by statutory consumer protection regimes. Unlike some jurisdictions like the United States, Canada lacks a comprehensive strict liability regime for defective products (with the exception of Quebec that has a separate regime of statutory liability that may be likened to a regime of strict liability), meaning plaintiffs must prove a failure to take reasonable care.

Negligence

Negligence law is the main source of product liability law in common law Canada. Canadian negligence law has developed mainly through the common law, though it has statutory components too. As a general principle, Canadian negligence law provides that product manufacturers, producers, and distributors owe a duty to take reasonable care to avoid causing foreseeable harm to consumers. This general principle finds expression in several more specific legal causes of action: negligent failure to warn, negligent design, and negligent manufacture.

Negligent failure to warn requires the plaintiff to show that the defendant knew or ought to have known about a danger associated with the product; the defendant negligently failed to provide a suitable warning; and the plaintiff would have heeded the warning and avoided a loss had a warning been made. Negligent design requires the plaintiff to show that the defendant failed to take reasonable care in the design of the product, which typically requires proof that a feasible and safer alternative design was available; and the failure to adopt that design caused the plaintiff to suffer loss. Negligent manufacture is similar to negligent design, except it relates to negligence in the manufacturing process, rather than in the design process.

Defendants may raise a number of potential defences to negligence claims. For example, defendants may argue that the product at issue was not defective, the defendants exercised reasonable care in designing or manufacturing it, no safer designs were practically feasible, the alleged negligence did not cause the plaintiff’s alleged loss, and/or the plaintiff suffered no loss at all. The defences raised depend on the circumstances.

In product liability cases, the ordinary remedy is a damages (monetary) award designed to place the plaintiff in the position they would have occupied had the wrong never occurred. However, other remedies such as injunctions or declaratory orders may also be appropriate in some circumstances.

Statute

In addition to common law negligence principles, statutory consumer protection regimes provide a secondary source of law that plaintiffs can use to bring product liability claims. For example, provincial consumer protection statutes generally prohibit deceptive and/or unconscionable acts in relation to the supply of goods or services for personal, family, or household purposes. These statutes generally allow consumers to seek monetary and other relief in court. As another example, some Canadian provinces have passed product-specific statutes allowing governments to sue for costs incurred as a result of product-related wrongs.

Quebec

Product liability law in Quebec is primarily governed by the Civil Code of Québec (CCQ) and the Quebec Consumer Protection Act (CPA). The CCQ establishes that manufacturers are presumed liable for an injury caused by a safety defect in their products (Article 1468 CCQ). A safety defect is defined as any condition in which the product does not afford the safety that a person is normally entitled to expect, including defects in design or manufacture, poor preservation or presentation, or the lack of sufficient indications as to the risks and dangers it involves or as to the means to avoid them (Article 1469 CCQ). To succeed in a product liability claim under the CCQ, the plaintiff must prove the existence of a safety defect, a resulting injury, and a causal link between the two. Once these elements are proven, the law presumes liability, unless the defendant can establish one of the two following grounds of exemption (Article 1473 CCQ): the victim knew or could have known of the defect, or could have foreseen the injury; or according to the state of knowledge at the time that the defendant manufactured, distributed or supplied the good, the existence of the defect could not have been known and they were not neglectful of their duty to provide information when they became aware of the defect.

In the consumer context, the Quebec CPA provides additional and sometimes overlapping protections. Most notably, section 53 CPA creates a statutory presumption of liability for the seller, distributor, and manufacturer when a consumer suffers injury due to a defect in a product. Under this provision, the consumer does not need to prove fault; it is sufficient to show that the product was defective and that the defect caused the injury. Once that is established, the onus then shifts on the manufacturer to demonstrate either that the danger was apparent, or that it was known (or ought to have been known) by the consumer. This provision mirrors the strict liability approach of Articles 1468 and 1469 CCQ, but is specifically tailored to consumer transactions, and it extends these same protections to a subsequent purchaser of the good (section 53 CPA). In addition, sections 37 and 38 CPA impose a legal warranty of quality, requiring that goods be fit for their ordinary use, durable for a reasonable period, and free of hidden defects. If these standards are not met, consumers may seek remedies such as repair, replacement, rescission, as well as compensatory and punitive damages (section 272 CPA). Together, the CCQ and CPA create a comprehensive and consumer-friendly framework for product liability in Quebec, shifting much of the burden of proof onto manufacturers and sellers.

Class actions

A plaintiff alleging negligence or statutory causes of action may seek to bring their claim via class action, a proceeding that allows a class of people to seek relief through a single proceeding brought on behalf of the class. In recent decades, class actions have played an increasingly important role in Canadian product liability cases, especially in relation to mass tort claims arising from widely used products. However, a product liability claim may proceed via class action only if certain statutorily prescribed conditions are met. While these conditions vary from province to province, they generally require the plaintiff to show:

• a reasonable cause of action;
• a class of two or more people;
• common issues;
• a preferable procedure; and
• a workable litigation plan and a suitable representative plaintiff.

Under strict liability regimes, defendants may be found liable for injuries without plaintiffs needing to prove fault on their part. As mentioned above, Canada (other than Quebec) does not have a comprehensive strict liability regime for defective products. It has also not developed, to date, specific strict liability rules for AI products. Nonetheless, certain authors have suggested courts could find fruitful analogies in existing jurisprudence relating to strict liability in cases where animal owners were held strictly liable for injuries caused by their animals or in cases involving vicarious liability of employers for the acts of their employees (www.papers.ssrn.com/sol3/papers.cfm?abstract_id=3734655).

As mentioned above, in Quebec, the product liability regime may be likened to a regime of strict liability. While no AI class actions have been commenced in Quebec under this theory of liability, with plaintiffs opting instead to base their actions on intellectual property (www.registredesactionscollectives.quebec/fr/Fichier/Document?NomFichier=15812.pdf, www.registredesactionscollectives.quebec/fr/Fichier/Document?NomFichier=15664.pdf) and privacy rights (www.canlii.org/fr/qc/qccs/doc/2024/2024qccs3968/2024qccs3968.html), the regime for product liability under the CCQ and CPA could likely apply to AI systems.

The application of the product liability principles discussed above to AI products in Canada remains an emerging area of the law. Although AI products are playing an increasingly important role in the Canadian economy, Canadian courts have not yet considered how existing product liability principles may apply to claims relating to AI products. Nor have Canadian policy makers passed AI legislation prescribing principles that apply to these claims. That said, US courts have started to grapple with a first wave of AI product liability cases, and US product liability cases are often a precursor to equivalent cases in Canadian courts, though the two countries have materially different product liability frameworks.

Some have suggested that Canada’s existing product liability law framework is well suited to AI product liability cases. But even if Canadian courts accept this position, many unanswered questions remain. For example, in AI product liability cases, the standard of care may be difficult to determine given the rapid pace of AI innovation, the range of different AI systems and their potential uses, and the absence of any universally accepted principles governing the design or deployment of AI systems. The nature of AI technologies may also prove challenging for existing liability regimes, not least because of the increasing autonomy of AI agents, the opacity of AI decisions and their emergent, unpredictable behaviour. Similarly, the nature and scope of necessary warnings on the use of AI systems may be difficult for these same reasons and causation may likewise be difficult to determine given that multiple factors beyond the use of AI systems may cause or contribute to the alleged harm.

Canadian courts may also face complex questions about who (if anyone) is liable for harms caused by AI. For example, in a scenario where one company designs an AI system, another company adapts that AI system to a particular use, and an individual uses that AI system in a way that results in a loss, courts may need to apportion fault to the different parties involved, which may present challenges in the absence of clear principles given the design, adaptation, and use of AI systems. However, one of the strengths of the common law is its flexibility and adaptability, which allow the law to respond appropriately to different circumstances.

Quebec civil law appears at first glance well equipped to deal with the novel issues posed by generative AI. With a separate regime for liability under the CCQ and CPA that is akin to strict liability regimes, and with privacy rights under the Quebec Charter that are not available elsewhere in the country, Quebec is a friendly jurisdiction for AI class actions. However, the Quebec product liability regime remains untested given the recent emergence of these technologies and the time that it takes for a class action to reach resolution.

The Canadian legislative landscape witnessed important changes in 2025. On June 16, 2022, the federal government had introduced Bill C-27, titled The Digital Charter Implementation Act, 2022, which included the Artificial Intelligence and Data Act (AIDA). The AIDA would have become Canada’s first law specifically dedicated to regulating AI; however, AIDA died on the order paper 2025 with the prorogation of the federal Parliament (www.gazette.gc.ca/rp-pr/p2/2025/2025-01-06-x1/html/si-tr9-eng.html). The new Liberal government led by Prime Minister Mark Carney, elected on April 28, 2025, has not yet introduced a replacement bill. However, the tabling of Bill C-36 (which, as discussed above, seeks to modernize federal privacy law) and Bill C-34 (which introduced also in June 2026 the Safe Social Media Act) have confirmed the federal government is moving away from a comprehensive AI law toward the regulation of AI risks through privacy and digital platform laws. Unlike its predecessor, Bill C‑27, Bill C-36 does not include a stand‑alone artificial intelligence regime.

Separately, Bill C‑34 would establish a new digital safety framework for regulated social media platforms, chatbot services and certain online services (www.canada.ca/en/canadian-heritage/services/safe-social-media-act.html). The bill places particular emphasis on child safety, including restrictions on social media access for children under the age of 16 and new obligations for service providers, alongside broader governance and transparency requirements backed by significant monetary penalties.

ch (CIFAR), notably famous for its visionary support of early machine and deep learning research (www.newswire.ca/news-releases/canada-funds-125-million-pan-canadian-artificial-intelligence-strategy-616876434.html). Innovation, Science and Economic Development Canada (ISED) unveiled phase 2 of this strategy in June 2022, committing an additional CAD 443 million over 10 years to go beyond research and into supporting commercialization and adoption of AI in a responsible manner. Still under CIFAR’s oversight, this second phase is notably focused on the funding of top AI institutes working on commercialization of AI solutions (CAD 60 million), the funding of Canada’s Global Innovation Cluster to develop made-in-Canada AI (CAD 125 million) and the attraction of top talents (CAD 160 million) (www.canada.ca/en/innovation-science-economic-development/news/2022/06/government-of-canada-launches-second-phase-of-the-pan-canadian-artificial-intelligence-strategy.html). In June 2026, the federal government launched its new National AI Strategy: AI for All. This strategy builds on the two previous Pan-Canadian AI strategies by focusing on responsible AI adoption, economic growth and technological sovereignty. The strategy is organized around six pillars aimed at fostering trust, opportunity and sovereignty, with a particular emphasis on protecting Canadians from AI-related risks. The strategy sets ambitious objectives, including increasing business adoption of AI from 12% to 60% by 2034, creating up to 90,000 AI-related jobs and work placements for young Canadians, supporting the creation of up to 250,000 additional jobs through AI adoption, and generating nearly CAD 200 billion in GDP gains through increased productivity. It also includes plans to expand sovereign AI infrastructure, including the development of a world-leading Canadian supercomputer and enhanced access to computing resources for Canadian businesses. (www.ised-isde.canada.ca/site/ised/en/canadas-national-artificial-intelligence-strategy-ai-all.)

The federal government’s 2024 budget included a host of additional measures to grow Canada’s AI advantage, most significantly with the announcement of a CAD 2 billion to support the newly created AI Compute Access Fund and the Canadian AI Sovereign Compute Strategy. Building on this foundation, the federal Budget 2025 committed an additional CAD 925.6 million over five years to further develop large-scale sovereign public AI infrastructure. This new funding, which includes CAD 800 million re-allocated from the 2024 commitments alongside fresh capital, seeks to ensure that public and private researchers have secure, domestic access to the compute power necessary for innovation. As a point of comparison, phase 2 of the Pan-Canadian AI Strategy had only dedicated CAD 40 million on computing initiatives. This trajectory underscores the evolution of Canada’s AI strategy from an approach focused primarily on talent acquisition to a comprehensive model seeking to build the sovereign industrial backbone and public-sector adoption framework needed to sustain long-term AI growth.

Consequently, Canada has a broad AI strategy that is supported with substantial funding and legislative initiatives.