The Legal Pitfalls of Using Human DNA and Tissue in Quebec-based Biotechnology Projects

June 10, 2026

Biotechnology projects rely on sensitive genetic data and biological material

Nowadays, innovation-driven companies involved in life sciences, research and biotechnology handle some of the most legally sensitive assets: human tissue, biological material and genetic data. Innovation models involving tissue engineering, biobanks or AI-based analytical technologies are now based on the transfer and use of biological data with high scientific and commercial value.

Yet, many organizations still prioritize the scientific and operational aspects of their projects without giving sufficient consideration to the legal restrictions that arise when a project involves a person’s DNA or biological material.

From a business standpoint, the risk is that an organization—whether a private company or a public institution—might develop a technology, but then be unable to market that technology because it does not hold the necessary rights to use the biological material and information involved.

In Canada, and particularly in Quebec, laws that protect personal and health information have become central to such projects.¹ We are no longer simply dealing with typical cybersecurity or privacy concerns. These laws directly affect how biological material is:

collected
used
transferred
stored
altered
and potentially leveraged for commercial or collaborative research purposes².

Why DNA and human tissue are subject to a particular legal protection

The highly sensitive nature of DNA and genetic data is no longer disputed. Canadian case law has long recognized the highly personal and private nature of this type of information.³ It also emphasizes the fact that human tissue and genetic data play a unique role in research and innovation projects because of the identification risks they carry, their scientific value, and the ethical and commercial concerns related to their use.⁴

This perspective is evident in section 2 of the Act respecting health and social services information⁵, for example, which defines health information as any information that concerns “any material taken from [a] person,” including biological material. Section 5 and following of this act set out the conditions under which such information may be used, disclosed or transferred in the context of research or collaboration involving third parties⁶

These obligations supplement those set forth in the Act respecting the protection of personal information in the private sector,⁷ which requires in particular that personal information be collected for specific and legitimate purposes, and that it be used in a manner consistent with the purposes for which it was originally collected.⁸

Artificial intelligence, genetic data and the risk of re-identification

From a biotechnology perspective, the matter becomes particularly touchy when human tissue or genetic data, which was initially collected for clinical or scientific purposes, is then used for technology or artificial intelligence projects. In fact, many projects that utilize artificial intelligence require not only biological samples and DNA, but also phenotypic data, health information and family history information from the patients from whom the biological samples were obtained. As such, there is a real risk of data cross-referencing here that must be managed with full awareness of the potential impact on those individuals.

In certain projects, combining DNA with family information could compromise the privacy of not only the individuals from whom the biological material was collected, but also their family members. This problem has already been raised in relation to genetic genealogy.⁹

Consent, health information and secondary uses tend to be overlooked

A project that was initially intended for research purposes can quickly drift into secondary uses that extend beyond its original scope. However, consent obtained at the outset does not necessarily cover all future uses, particularly where derived data or analysis results are integrated into technology platforms or used to develop analytical tools.¹⁰

Research agreements and biological material transfer agreements constitute an essential governance mechanism

Agreements have thus become the key governance mechanism. Biological material transfer agreements, collaborative research agreements and data-related provisions are no longer solely intended to protect intellectual property or commercial confidentiality. They also serve to define the processes involved in transferring biological samples, ensuring data traceability, imposing restrictions on reuse and meeting anonymization requirements.¹¹

The rights relating to intellectual property, DNA and personal information are interconnected

The interplay between biotech innovation, intellectual property and personal information protection raises complex legal issues. A genetic database or a biological model derived from it can be both a strategic business asset AND a collection of highly sensitive personal information.

However, any intellectual property rights that may apply to the results, algorithms or analytical methods do not exempt organizations from the obligations set out in Quebec laws regarding the protection of personal and health information¹². On the contrary, in order to market a technology, organizations must hold not only the necessary intellectual property rights but also the rights required under the legal framework governing health and personal information.

The commercialization of a technology begins long before it is brought to market

As organizations increasingly seek to leverage data from scientific research, issues related to the governance of human tissue, DNA and biological material should no longer be treated as a secondary consideration addressed only at the end of a project. They are becoming an integral part of the legal, operational and commercial framework of modern biotechnology projects and therefore deserve careful consideration from the outset.

Summary

1. From a legal standpoint, DNA is considered to be health information

In Quebec, biological material and genetic data are not merely instruments of research. Under the law, they are defined as highly sensitive “health information”. The collection, use and transfer of this type of information is strictly regulated and requires explicit, informed consent.

2. Intellectual property does not confer all rights to the holder

Just because a company develops a high-performance AI algorithm or an innovative biological model does not mean it can circumvent Quebec’s privacy laws. Bringing biotech products to market requires holding the necessary intellectual property rights AND complying with the legal framework governing the use of health data.

3. The pitfall of project drift (secondary uses)

Consent obtained at the outset of a clinical research project usually does not extend to future uses, such as the integration of data into AI platforms. Organizations that fail to establish a solid contractual framework (e.g., transfer agreements, anonymization clauses) from the start may never be able to market their technology.

Act respecting health and social services information, CQLR c R-22.1, ss. 1, 2, 5, 44 to 49 and 77.
Act respecting the protection of personal information in the private sector, CQLR c P-39.1, ss. 4, 5, 8, 12 and 14.
R. v. Dyment, 1988 CanLII 10 (SCC), [1988] 2 SCR 417
Marie Hirtle and Bartha Maria Knoppers, Le stockage des éléments du corps humain, les droits de propriété intellectuelle et les autres droits de propriété, Industrie Canada, 2014.
Act respecting health and social services information, supra, note 1, s. 2.
Id., ss. 5, 44 to 49 and 77.
Act respecting the protection of personal information in the private sector, supra, note 2.
Id., ss. 4, 5, 8, 12 and 14.
Clausius, K., Kenny, E. & Crawford, M. J. (2023). BILL S-231: The Ethics of Familial and Genetic Genealogical Searching in Criminal Investigations. Canadian Journal of Bioethics / Revue canadienne de bioéthique, 6(3-4), 44–56.
Act respecting health and social services information, supra, note 1, ss. 44 to 49; Act respecting the protection of personal information in the private sector, supra, note 2, ss. 12 and 14.
Act respecting health and social services information, supra, note 1, ss. 48 and 49; Act respecting the protection of personal information in the private sector, supra, note 2, ss. 18.3 and 23.
Act respecting health and social services information, supra, note 1, ss. 5 and 49; Act respecting the protection of personal information in the private sector, supra, note 2, ss. 12, 17 and 18.3.

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Eric Lavallée
Partner, Lawyer and Trademark Agent
Kiran Younas
Lawyer

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May 6, 2026

Eight partners recognized as leaders in Canada for their expertise in infrastructure, according to Lexpert

On May 6, 2026, Lexpert recognized the expertise of five of our partners in its 2026 Lexpert Special Edition:Infrastructure. Laurence Bich-Carrière, Jean-Sébastien Desroches, Christian Dumoulin, Nicolas Gagnon, Édith Jacques, Marc-André Landry, Ouassim Tadlaoui and André Vautour now rank among Canada’s leaders in supporting economic players in the infrastructure industry. Laurence Bich-Carrière, is a member of the Quebec and Ontario Bar Associations, Laurence Bich-Carrière practices within the Litigation and Dispute Resolution group, in a versatile civil and commercial litigation practice specializing in complex litigation (class actions, appeals, extraordinary remedies, private international law). Jean-Sébastien Desroches practises business law and focuses primarily on mergers and acquisitions, infrastructure, renewable energy and project development as well as strategic partnerships. He has had the opportunity to steer several major transactions—complex legal operations, cross-border transactions, reorganizations, and investments—in Canada and at an international level on behalf of Canadian, American, and European clients and international corporations and institutional clients in the manufacturing, transportation, pharmaceutical, financial, and renewable energy sectors. Christian Dumoulin is a partner in the firm’s Business Law Group. He served as Managing Partner of the Sherbrooke office from 2015 to 2023 and sat on the firm’s Board of Directors from 2015 to 2017, and again in 2024. He specializes in mergers and acquisitions, commercial law and real estate law, and acts as a business and strategic advisor to clients of all sizes. Recognized for his rigour and practical approach, he supports clients in complex transactions (reorganizations, financings, sales and acquisitions, shareholder disputes). He has also been advising on infrastructure projects for more than fifteen years, including public-private partnerships, as well as on all aspects of commercial real estate law. Nicolas Gagnon focuses his practice on construction law and suretyship. He counsels contractors, public and private sector clients, professional services firms as well as surety companies at every stage of construction projects. He advises clients on the public bidding and procurement processes and participates in the negotiation and drafting of contractual documents involving various project delivery methods, such as public-private partnership projects and design, construction, financing and maintenance contracts. In addition to advising various construction industry stakeholders on construction management and any claims that may arise, he also assists them with dispute resolution processes. Édith Jacques is a partner in the Business Law Group in Montréal. She specializes in mergers and acquisitions and commercial and international law. Édith acts as strategic business advisor for medium to large private companies. Marc-André Landry is a member of the Litigation and Conflict Resolution group and focuses his practice on commercial litigation. He frequently assists his clients in resolving their disputes through negotiation, mediation or arbitration, or before the various courts of law. Over the years, he has represented businesses in many sectors, including construction, real estate, renewable energy, conventional energy, new technologies, financial services and pharmaceuticals. Ouassim Tadlaoui is a partner in the firm’s Litigation and Dispute Resolution Group and focuses his practice on banking litigation, restructuring, bankruptcy and insolvency, as well as construction surety. He represents chartered banks and other financial institutions and alternative lenders as creditors, as well as certain debtors, in bankruptcy and restructuring proceedings. He also represents and advises surety companies, as well as national and international businesses, in insolvency, bankruptcy and restructuring matters in the construction industry. André Vautour practises corporate law and commercial law, and is specifically interested in corporate governance, strategic alliances, joint ventures, investment funds, and mergers and acquisitions of private companies. He also practises technology law (drafting technology development and transfer agreements, licensing agreements, distribution agreements, outsourcing agreements, and e-commerce agreements). About Lavery Lavery is the leading independent law firm in Quebec. Its more than 200 professionals, based in Montréal, Québec City, Sherbrooke and Trois-Rivières, work every day to offer a full range of legal services to organizations doing business in Quebec. Recognized by the most prestigious legal directories, Lavery professionals are at the heart of what is happening in the business world and are actively involved in their communities. The firm’s expertise is frequently sought after by numerous national and international partners to provide support in cases under Quebec jurisdiction.

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June 8, 2026

AI for All: Canada focuses on responsible adoption

On June 4, Canada unveiled AI for All, its new national artificial intelligence strategy. Anchored in the principle that the benefits of AI must accrue to all Canadians, the strategy emphasizes the democratization of AI through expanded training, governance, and Canadian infrastructure. A strategy centred on adoption Canada’s new national AI strategy rests on the premise that the benefits of AI will materialize only if individuals, businesses, and institutions are able to use it with confidence. The government notes a significant gap between AI’s potential in Canada and its actual adoption. According to Statistics Canada, only 12% of Canadian businesses used AI to produce goods or services between mid-2024 and mid-2025. The stated objective is to increase that rate to 60% by 2034. To achieve this, the strategy is structured around six pillars: protecting Canadians and safeguarding democracy, developing skills, promoting adoption, building sovereign infrastructure, supporting Canadian AI champions, and strengthening trusted international partnerships. Trust as a driver of adoption The strategy emphasizes that trust is not presented as an obstacle to innovation, but rather as a condition for its adoption. In particular, the strategy provides for the modernization of privacy rules, protection against certain online harms, transparency of AI systems, and strengthened capacity for the Canadian AI Safety Institute. The government also announced its intention to advance measures such as watermarking AI-generated content, transparent model evaluation, a Canadian certification program for trustworthy AI, and support for the standards ecosystem. For organizations, this confirms an underlying trend: AI will not be assessed solely on the basis of technical performance, but also on its transparency, security, governance, and protection of personal information. Training before broad deployment The strategy places significant emphasis on AI literacy. Canada plans to create a National AI Literacy Initiative, provide training content to one million post-secondary students and more than 3,000 teachers, and give post-secondary students access to trustworthy AI agents. This approach reflects the recognition that AI adoption depends not only on access to tools, but also on users’ ability to understand risks, limitations, biases, misinformation, and privacy issues. The strategy also extends training efforts to workers already in the labour market. It provides support for reskilling pathways for mid-career workers, practical training tailored to real-world AI use cases in workplaces, and employer-led training, particularly with the support of colleges, CEGEPs, and polytechnic institutions. The objective is to ensure that AI adoption benefits workers by strengthening their skills, productivity, and ability to participate in the transformation of their organizations. Helping organizations move from experimentation to implementation This component of the strategy is aimed particularly at small and medium-sized enterprises. The government observes that many organizations are already experimenting with AI, but that the transition to sustainable integration remains more difficult. Identified barriers include costs, access to expertise, and uncertainty about the first steps to be taken. Measures announced include the Business Development Bank of Canada’s LIFT program, a $500 million initiative to help Canadian SMEs access the financing needed to integrate AI tools into their operations, as well as a $500 million investment to strengthen the Regional Artificial Intelligence Initiative. The strategy also provides for AI literacy and readiness assessment tools to help businesses identify concrete use cases. Health as the first “national mission” The government plans to launch an AI missions program. The first mission will focus on health, with $200 million allocated to improving outcomes in that sector. The strategy specifically targets applications related to access to care, wait times, avoidable visits, and reducing physicians’ administrative burden. This mission-based approach reflects a desire to concentrate efforts on concrete problems rather than treating AI as an abstract technology. It also seeks to bring researchers, businesses, governments, and practitioners together around measurable objectives. Sovereignty as a strategic issue The strategy presents digital sovereignty as a central issue. It notes that several essential capabilities (computing, cloud infrastructure, connectivity, data, and talent) remain largely located outside Canada. This dependence can expose sensitive data, intellectual property, and critical infrastructure to foreign rules or decisions. Canada plans, among other things, to build a world-class public supercomputer, expand sovereign computing and cloud infrastructure, and invest in secure digital systems for government operations. Supporting Canadian AI champions The strategy also links sovereignty to the ability to grow Canadian businesses domestically. The government states that, in order to retain its most successful entrepreneurs and companies, as well as its most valuable intellectual property, Canada must strengthen its investment environment, support domestic commercialization, and enable companies to compete from within Canada. In this regard, the strategy provides, among other measures, an additional $700 million in affordable sovereign computing capacity for Canadian SMEs, allowing them to develop, test, and deploy their products on Canadian infrastructure rather than relying primarily on foreign platforms. Establishing trusted international partnerships Finally, the strategy presents international alliances as a lever for resilience, sovereignty, and market access. Canada intends to work with trusted partners to develop shared AI capabilities, harmonize standards, support more resilient supply chains, and offer alternatives to closed systems or systems that are not aligned with democratic values. This direction includes expanding the Sovereign Technology Alliance, supporting open-source AI, and using diplomatic and trade networks to attract investment, promote Canadian champions, and open new markets. Key takeaways The publication of the AI for All strategy confirms a shift observed for several years: the question is no longer whether organizations will adopt AI, but how they will do so. For several years, discussions surrounding AI focused primarily on technological breakthroughs, investment, and competitiveness. Canada’s strategy instead emphasizes the conditions required for sustainable adoption: trust, skills, governance, infrastructure, and organizations’ ability to integrate these tools into their day-to-day operations. This shift is significant. The question is no longer only what AI is capable of doing, but how organizations can use it effectively, responsibly, and in a manner suited to their context. In this respect, the challenges facing organizations in the coming years will likely be less technological than organizational. The ability to develop a culture of innovation, train teams, implement appropriate governance mechanisms, and maintain stakeholder trust may prove just as decisive as the choice of tools themselves. The federal strategy also acknowledges a reality that is becoming increasingly clear: AI is no longer solely a matter of productivity or innovation. It is gradually becoming an issue of sovereignty, economic resilience, and long-term competitiveness. In this context, it will be particularly interesting to observe how organizations, institutions, and the various levels of government translate these directions into concrete practices. As is often the case with innovation, true transformation will not arise solely from the technologies that exist, but from the way they are integrated into the processes, decisions, and relationships of trust that shape our organizations.

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March 18, 2026

Behind the Scenes of Sports, Data Never Takes a Break

The World Anti Doping Agency suffered a data breach in 2016—a vivid illustration that even the most prominent sporting institutions are not immune to cyber incidents. The authorities have now formalized what was previously just an observation: In a bulletin published in 2024, the Canadian Centre for Cyber Security warned that the entire sports ecosystem—spectators, athletes, organizations and government representatives—is the target of cyberattack campaigns. Malicious actors will attempt extortion through business email compromise, ransomware attacks, phishing, malicious websites and search engine poisoning, among others. Take heed, as when an incident occurs that is serious enough to require a report to the authorities, it is often too late to establish sound governance and engage in due diligence. The sporting competitions of today are producing massive amounts of data. The quantity is staggering, and the data itself almost Orwellian. Check the tables below to see for yourself. Data collected on athletes League Information collected NFL Performance data (statistics, position and movement metrics, speed, and passing, rushing and receiving yards) Medical and/or health data (examinations, injuries, concussion protocols) Substance screening data Data on disciplinary actions and investigations Professional and contractual data Travel, logistics and security data NHL Performance data Medical and/or health data (examinations, injuries, concussion protocols) Substance screening data Data on disciplinary actions and investigations Professional and contractual data Travel, logistics and security data MLB Performance data Medical and/or health data (examinations, injuries, concussion protocols) Substance screening data Data on disciplinary actions and investigations Professional and contractual data Travel, logistics and security data Collection of customer information online League Information collected NFL Information provided by individuals Identifiers: name, email, address, telephone number, date of birth; unique identifiers (username, password, SSN and other government identifiers if required, e.g. for awards) Demographic data and other protected categories: gender, race, ethnicity, sexual orientation Financial and commercial information: payment data, purchase history Real-time geolocation; precise geolocation Communication and marketing preferences Favorite team and inferences about preferences Audio, electronic and visual information (e.g., photos provided) Biometric data, if you opt for biometric authentication at the stadium; with consent and additional notice if required Information about your contacts (name, email) that you share; if authorized, access to your contacts, calendars and photos Search queries Content posted (comments, forums) Professional and employment information Education information Information that may be health-related (e.g., accessible seating) Correspondence, waivers, consents and other information sent Automatic collection Device and network identifiers and technical data: IP address, MAC address, advertising identifiers, device type, browser, OS Usage: page views, links clicked, browsing journeys, application usage data Tracking and emails: cookies, pixels, tags, interaction with emails (opened emails, clicks) Social media (if linked): data received according to your settings and the platform’s policy Logs and traffic: server logs, stadium Wi-Fi traffic Video and audio recordings: CCTV and pictures taken or video recorded during events NHL Information provided by individuals Identifiers and contact information (name, email, telephone number, address, date of birth) Commercial information (payments, purchases, services) Demographic data (language, age, gender, race, ethnicity, household composition and income) Preferences (favourite team, favourite players) Photos and/or videos Content, feedback (comments, surveys) Contact information of friends Application data (resume, references, checks permitted) Automatic collection Activity and interactions (content viewed, bids, purchases, time spent, cookies, tags), access methods (browser, OS, IP address, browsing history before and after) Device information and identifiers (type, unique identifiers, local content if allowed) Location (GPS, Bluetooth, Wi-Fi, cells) Inferences about preferences Commercial information about transactions (e.g., timestamps) Collection from third parties Member clubs (ticketing, login credential, usage logs) Fanatics, NHL Shop, NHL Auctions (name, email, items purchased; marketing engagement statistics) Other business partners, public sources, commercial sources (data brokers) Connected social media (according to the platform’s settings and policies) NHL teams* Contact information: name, email address, home address, gender, date of birth, telephone number (e.g., ticket purchase, ticket transfer, account creation, inquiries, contests, promotions) Demographic data and preferences (age group, race, gender; preferred events, preferred products, e.g., surveys) Health data related to accessibility needs Video surveillance in venues (security; sharing limited by law) Anonymous traffic analysis and device counting (cameras, technological devices; Wi-Fi); statistics that can be shared with partners Depersonalized web analytics (Google Analytics); opt-out option Online advertising and/or remarketing (Google, Facebook, LinkedIn, etc.) through cookies; opt-out mechanisms (platform settings; DAAC) Geolocation through applications if enabled Social media: profile data and authorized interactions Technical data (IP, browser, OS, resolution, location, language, origin, keywords, pages viewed, data entered, ads viewed), identifiers (IDFA, AAID), connection information (operator, ISP, Wi-Fi); ability to recognize a device) MLB Information provided by individuals Identifiers and contact information: full name, email address, home address, telephone numbers, date of birth Security and authentication: password Payments: payment details Demographic data: demographic characteristics Content and recordings: voice recordings, audiovisual recordings Preferences and interests: information about your interests and preferences Activity and event related data: information requested for an activity or event (e.g., emergency contact) Sensitive personal information: as defined by applicable laws (e.g., racial or ethnic origin; health information such as disabilities or allergies) Automatic collection Technical and usage data: IP addresses, device data, usage data Location and contacts: location data; contacts saved on your mobile device Collection from third parties Data from third parties and integrations: information provided by other companies if individuals connect their services * This data is collected about website users, people who visit venues, people who apply for jobs or participate in contests, people who submit drafts. How leagues are structured Regarding privacy and personal information, we must look at how sports leagues are organized to understand who does what. In most cases, sports leagues are non-profit organizations or corporations. An entire framework of rules is built around these structures, defining both how governance is done and what business model is used. First, there are the articles of association and by-laws, which dictate governance, team admissions, voting rights, and the powers of the commissioner or board of directors. There are also the sporting and competition regulations regarding eligibility, game schedules, transfers, drafts, salary caps and cost control mechanisms. The leagues also adopt integrity and security policies against doping, betting and manipulation, harassment and abuse, as well as commercial agreements covering broadcasting, sponsorships, ticketing and data leveraging, among others. There can also be collective agreements with players’ associations and formal dispute resolution mechanisms. In this environment, the league plays a central role. It generally has the power to adopt, interpret and amend its rules; admit teams; manage expansion and relocation projects and changes of control; as well as the power to impose sanctions such as fines, point deductions, suspensions or exclusions. It also centralizes strategic commercial rights, media rights, trademarks and data, and it implements revenue-sharing policies designed to maintain a competitive balance between teams. Personal information: the roles of each Teams In day-to-day relations with athletes and customers, teams are generally the main point of contact. They sign contracts with players, sell tickets, manage subscriptions and operate online stores and loyalty programs. In practice, teams are often the ones that collect personal information, that explain what the information is used for, that decide what information needs to be collected and that put in place security and incident management measures. Teams must therefore be able to clearly inform athletes and customers about the purposes for which personal information is collected, the means by which it is collected, the categories of information collected, who receives the information, and the rights that athletes and customers have. Teams must limit collection to what is necessary. They must ensure that information is accurate; they must obtain valid, manifest, free, informed and explicit consent for sensitive information such as health or biometric data; they must implement security measures adapted to risks; they must manage and report confidentiality incidents likely to cause serious harm; they must respond to requests for access and rectification; and they must stringently govern the sharing of information with service providers and mandataries. Athletes and customers often see the team as the true holder of their data. Leagues The role leagues play regarding personal information is more difficult to understand, as it varies depending on activities. When a league directly collects information from an individual, for example through an official application, a broadcasting platform or a transactional site for its own purposes, it must assume responsibilities comparable to those a team has. This is what MLB Advanced Media does, for example, defining itself as a “data controller” with respect to its customers’ data. But in many cases, the league acts behind the scenes. In some respects, it acts as a mandatary for the teams, negotiating and signing technology contracts, broadcasting agreements and other commercial agreements that will be used by the teams. In other respects, it acts as a service provider, offering centralized technology platforms, ticketing systems, data infrastructure and shared administrative services. Under Quebec law, these two roles—mandatary and service provider—are treated the same: The team can transmit to the league the information it needs to perform the mandate or service contract without having to ask for the consent of each person again, provided that a written agreement imposes clear measures to protect privacy, limits the use of data to the sole purposes of the mandate or service and governs data retention. The league must also promptly inform a team’s privacy officer of any privacy breach or attempted privacy breach and allow the officer to conduct checks. Also, teams and the league can always choose to base certain exchanges of information on the explicit consent of athletes or customers. However, such consent must be genuinely explicit, free, informed, given for specific purposes and presented separately when asked to be given in writing. Conclusion Although professional leagues are the ones in the spotlight, the same logic applies to amateur or non-professional sports organizations. In all cases, the relationship between the league, the team and the athlete or customer must be clearly governed from a privacy standpoint. Sports organizations should map the flow of personal information, harmonize the information messages they give to the those concerned, establish a standard agreement governing the sharing of information between teams and the league, provide simple mechanisms for access and rectification, and have key employees trained in privacy matters. Incorporating these points into articles of association, by-laws and team and league agreements will reduce risks and strengthen the confidence of athletes, parents, fans and business partners. Yet, a fundamental question still remains: Given that by law, data can only be collected for serious and legitimate reasons (necessity criterion), is the mass of information currently collected in the sports ecosystem really warranted? Sports organizations will have no choice but to delve into this strategic issue.

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February 26, 2026

Anatomy of AI projects from the vantage point of export controls

In a previous Bulletin, the authors broadly outlined the legal framework that applies to export controls, as well as the challenges surrounding large language models in artificial intelligence in an era of knowledge sharing. Given that a number of legal and geopolitical developments covering various aspects of this topic took place in 2025, a brief overview is timely on the potential implications for the development of your AI projects, with a special mention of generative AI (or “GenAI”), as the new year begins. What are export controls? Export controls establish rules designed to curb the risk of transferring military, strategic and dual-use (civilian and military) goods and technologies to destinations deemed contrary to national security interests. Such technologies can take on various forms, ranging from physical hardware to technical information. In Canada, export controls are based on a licensing system, under which permits are given based on a series of items listed on the Export Control List (“ECL”) under the Export and Import Permits Act (“EIPA”). To find out if parts of your AI projects are subject to export controls, you should primarily (but not exclusively) refer to that list and to the guide prepared to better understand the list. Key events in 2025 Order SOR/2025-89 On March 7, 2025, an Order amending the ECL was published in the Canada Gazette, in an effort to include emerging technologies that are increasingly faster and more scalable, the capabilities of which raise concerns about potential adversarial military applications.1 Of particular interest in this context, subitem 5506(1) of the schedule to the ECL has been replaced by a number of paragraphs and subparagraphs. But what do these changes mean for AI projects in practice? The amendments made to subitem 5506(1) do not target AI applications (algorithms, models, data), but rather: extreme ultraviolet (“EUV”) lithography equipment, namely EUV masks and reticles making it possible to use this technology to manufacture advanced integrated circuits; cryogenic cooling equipment and ultra-sensitive amplifiers for quantum computers; advanced semiconductor materials; development and production softwares related to certain of the foregoing technologies.2 In other words, subitem 5506(1) targets the industrial toolbox used to build advanced computers, in particular through its inclusion of EUV lithography, which is used for cutting-edge integrated circuits and quantum computers that are revolutionizing the world of advanced computing. It can therefore be said that these rules affect the AI industry because of a form of hardware dependence, since tight control over these infrastructure manufacturing technologies necessarily affect the ability of a country or company to develop and operate advanced AI. In sum, these latest amendments are simply the continuation of those made in the previous year’s Order, which targeted the fields of quantum computing and advanced semiconductor manufacturing in particular (GAAFETs, representing next-generation integrated circuits).3 It has yet to be ascertained how the aforementioned orders will directly affect typical GenAI projects (model development, AI SaaS services, etc.). Those who will experience the more direct repercussions are suppliers of advanced computing equipment and businesses doing R&D on semiconductors, integrated circuits and quantum computing. Notice to Exporters No. 1159 Apart from the technical components, a certain complexity arises when we understand that the definition of a “technology” subject to export controls within the meaning of the law is meant to be broad, and that it includes technical data, technical assistance and information necessary for the development, production or use of an item appearing on the ECL. In other words, the scope of the technologies concerned goes beyond simple physical components or equipment. This is especially true given the proliferation of often cross-border cloud-based solutions, which make technical knowledge accessible digitally and circulate it far and wide. Given this context, it is appropriate to read the Guidance on the movement to and storage of controlled technology in the Cloud (Notice to Exporters No. 1159), published in November 2025 by the Government of Canada. The document was prepared to clarify instances when the use of cloud services constitutes a transfer of controlled technology under the EIPA, requiring a permit.4 In summary, the guidelines state that: it may be considered a transfer if a controlled technology is disclosed from a place inside Canada to a place outside Canada; a controlled technology is considered disclosed if it is sent from Canada and stored in a foreign location in a way that creates a reasonable possibility that a person located outside Canada would be in a position to examine that technology; a reasonable possibility means more than a mere possibility, but less than the standard of “more likely than not”; the location of servers hosting controlled technology only matters if it affects the reasonable possibility that the technology could be disclosed outside Canada; in general, it is considered a transfer when a person located outside Canada holds decryption keys or routine access rights that create more than a remote possibility that the technology may be examined, or when a cloud service provider creates an unencrypted backup copy that contains controlled technology to restore a system after an incident, and that such copy is stored on servers outside Canada where foreign administrators can access it; when cloud services are used, both the owner of the controlled technology and the cloud service provider have a degree of care and control of the technology. Thus, not only is there a risk of knowledge sharing where items directly listed on the ECL are involved (whether to manufacture them or otherwise), but the possibility of violating export controls also exists because of the interaction between cloud services and the knowledge that could be transferred (within the meaning set out above), if the cloud contains information about or relates to a controlled technology. Considerations regarding GenAI What about GenAI projects? Despite all of the above, these projects may still suffer indirect repercussions, and not only on highly technical components. You will need to exercise a certain degree of caution regarding the compliance of your GenAI projects because of the amount of information they can accumulate through the various layers of their structure. Training data There are the data used during the GenAI’s learning phase, before it is rolled out. The amount of this data can be massive, and it can be structured or unstructured. It is used to provide a knowledge base for the model and enable it to produce relevant outputs when it is given inputs. The learning phase is risky if the datasets contain controlled technical information and if the data can be regurgitated or combined when users use the GenAI. The GenAI’s weights, filters, and other operating parameters These parameters can be compared to physical control buttons—they are adjusted during the GenAI’s training and during the configuration of the solution that uses it. They determine how much each input element will influence the response and refine the model (i.e., the structure that allows the GenAI to interpret inputs and generate outputs). In the United States, weights in particular are a hot topic considering the country’s export policy, under which they can constitute key parameters for the most advanced AI models. Inputs This is the data provided by users to generate relevant outputs (e.g., text, images, structured data) when the GenAI is already rolled out. Such data is used to trigger a response or behaviour from the model. Just like with training data, inputs will be critical depending on the use made of the model and the information disclosed to obtain a response. Conditions consistent with legal requirements must be provided to prevent the model from being contaminated by sensitive data after it is rolled out, especially if it stores all the inputs provided to it for its continued learning. Outputs This is what GenAI generates in response to inputs. Outputs can be in the form of text responses or images, codes, or even data-based predictions. Given the above, it will be challenging depending on the datasets conveyed by the GenAI, to ensure that outputs do not violate export controls, as they could make it possible to indirectly obtain information the direct access to which would otherwise be prohibited. Conclusion We can imagine that the recent changes to export controls in Canada are just the beginning of an effort to address new concerns arising from this rapidly changing and ever more powerful technology. Export controls are also not devoid of a diplomatic context. For now, making AI subject to export controls seems to be the preferred mechanism to curb the exponential powers of such technology in Canada. The extent to which this will be done remains to be seen and will be interesting to follow. Government of Canada, Order Amending the Export Control List: SOR/2025-89 (March 7, 2025): Canada Gazette, Part II, Volume 159, Number 7: Order Amending the Export Control List: This is not an exhaustive list, but rather a few relevant examples that apply to advanced computing. Government of Canada, Order Amending the Export Control List: SOR/2024-112 (May 31, 2024): Canada Gazette, Part II, Volume 158, Number 13: Order Amending the Export Control List: Government of Canada, Notice to Exporters No. 1159 – Guidance on the movement to and storage of controlled technology in the Cloud (amended November 10, 2025): Notice to exporters no 1159 – Guidance on the movement to and storage of controlled technology in the Cloud

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December 2, 2025

AI in the Courtroom: A Call to Order in Specter Aviation

Eight quotes hallucinated by AI cost $5,000 for substantial breach (art. 342 C.C.P.) in the Specter Aviation case.1 While AI can improve access to justice, unverified AI use can lead to sanctions, adding to the risks unrepresented parties face. Quebec courts advocate for openness to AI, but with proper controls: AI is only useful when verified, traceable and supported by official sources. The cost of hallucinations On October 1, 2025, the Superior Court rendered judgment on a contestation to an application for homologation of an arbitral award rendered by the Paris International Arbitration Chamber (PIAC) on December 9, 2021. Under articles 645 and 646 C.C.P., the role of the Court in such a situation is limited to verifying whether one of article 646’s limiting grounds for refusal has been demonstrated. The applicant’s grounds—ultra vires, procedural irregularities, infringement of fundamental rights, public order, abuse of power—were deemed inappropriate and unconvincing. Although the decision is interesting in this respect, it is even more so in another one altogether. In his contestation, the unrepresented defendant relied on all possible support he could get from artificial intelligence. In response, the plaintiffs filed a table listing eight occurrences of non-existent citations, decisions never having been rendered, irrelevant references and inconsistent conclusions. Questioned at the hearing, the defendant did not deny that some references might have been hallucinated.2 In his judgment, Justice Morin turned the issue to principles. On one hand, access to justice requires a level playing field and the orderly and proportionate management of proceedings. On the other, even though unrepresented claimants or plaintiffs are given flexibility, never is forgery allowed: “Fabrication or shams cannot be tolerated to facilitate access to justice.”3 The Court therefore qualified the presentation of fictitious case law or fictitious quotes from authorities, whether intentionally or through simple negligence, as a serious breach that contravenes the solemnity that the act of filing of proceedings carries. It invoked article 342 C.C.P. to order the defendant to pay $5,000, to deter such conduct and protect the integrity of the process.4 Art. 342 C.C.P.: The power to punish substantial breaches Article 342 C.C.P. stems from the reform that was adopted in 2014 and came into force in 2016. Because this provision authorizes the court to impose a fair and reasonable sanction5 for significant breaches in the conduct of proceedings, it can be said to be punitive and dissuasive in nature. This power is distinct from the power granted by articles 51 to 54 C.C.P. which govern abuse of procedure, and an exception to the general regime of fees6 by which extrajudicial fees can be awarded, when warranted.7 A “substantial breach” must not simply be a trivial issue. It must reach a certain degree of seriousness, but it need not involve bad faith. It implies additional time and expense and contravenes the guiding principles of articles 18 to 20 C.C.P. (proportionality, control and cooperation).8 Nearly ten years later, case law illustrates a range of uses: $100,000 for the late filing of applications or amendments resulting in postponements and unnecessary work;9 $91,770.10 for a continuance on the morning of trial for failure to ensure the presence of a key witness;10 $10,000 for repeated delays, tardy amendment of proceedings and non-compliance with case management orders;11 $3,500 for the failure to or delay in disclosing evidence;12 $1,000 for filing an undisclosed statement in the middle of a hearing to take the opposing party by surprise.13 Sanctions and uses of AI in Canada and elsewhere Moreover, although the use of section 342 to sanction unverified use of technological tools appears to be a first in Quebec, a number of Canadian judgments have already imposed penalties for similar issues. In particular, they awarded: $200 in costs against an unrepresented party for having filed pleadings containing partially non-existent quotes to compensate for the time spent to make verifications.14 $100 in Federal Court, at the lawyer’s personal expense, for having quoted non-existent decisions generated by AI, without disclosing its use, further to the Kuehne + Nagel test.15 $1,000 before the Civil Resolution Tribunal of British Columbia to compensate for time needlessly spent dealing with clearly irrelevant, AI-generated arguments and documents in a case opposing two unrepresented parties.16 $500 and expungement of file containing AI-hallucinated authorities for non-compliance with the Federal Court’s AI policy.17 The $5,000 sanction ordered in this case was a deterrent; however, it is distinct from these other essentially compensatory amounts while in line an international trend, which can be observed in the following cases: On June 22, 2023, in the United States (S.D.N.Y.), a Rule 11 penalty of USD 5,000 was imposed along with non-pecuniary measures (notice to client and judges falsely cited), in Mata v. Avianca, Inc.18 . On September 23, 2025, in Italy, a sum of €2,000 was awarded ex art. 96, co. 3 c.p.c. (€1,000 to the opposing party and €1,000 to Cassa delle ammende), plus €5,391 in legal costs (spese di lite), by the Tribunale di Latina.19 On August 15, 2025, in Australia, personal costs of AUD 8,371.30 were ordered against the plaintiff’s lawyer, with referral to the Legal Practice Board of Western Australia, following fictitious citations generated by AI (Claude, Copilot).20 On October 22, 2025, in the United States (E.D. Oklahoma), monetary penalties totaling $6,000 were imposed on attorneys personally. They were also required to repay fees of $23,495.90, and some of their pleadings were stricken from the record with the requirement to refile verified pleadings.21 In addition to monetary penalties, Quebec courts have already identified a number of problematic situations related to the use of AI, such as: The Régie du bâtiment du Québec had to examine a 191-page brief containing numerous non-existent references. The author finally admitted to having used ChatGPT to formulate them. The commissioner underscored the resulting work overload and the need to regulate the use of AI before the RBQ.22 In a commercial case, the Court suspected hallucinated references and dismissed them, ruling on the credible evidence.23 At the Administrative Housing Tribunal (AHT), a lessor who had read translations of the C.C.Q. obtained through ChatGPT—which distorted its meaning—saw his application dismissed. However, his conduct was not found to be abusive, as his good faith was recognized.24 Two related AHT decisions noted that an agreement (a “Lease Transfer and Co-Tenancy Agreement”) had been drafted with the help of ChatGPT, but the AHT simply analyzed them as it usually does (text, context, C.C.Q. rules) and concluded that there had been a deferred lease assignment, without drawing any particular consequence from the use of AI.25 At the Court of Québec, a litigant attributed a self-incriminating formulation in his application to ChatGPT; the Court dismissed his explanation.26 In an application to have evidence set aside, the applicant claimed that he thought he was obliged to respond to investigators after having done research on Google and ChatGPT regarding his duty to cooperate with the employer just prior to the interview. The Court noted that he had been clearly informed of his right to remain silent and that he could leave or consult a lawyer. It therefore concluded that there was no real constraint and allowed the statement.27 Openness to AI with proper controls, certainly, but with a caveat These are just a few of a long and growing list of cases across Canada and the world around. Despite this trend, the decision in Specter Aviation avoids stigmatizing AI. The Court rather insisted on remaining open to AI, pointing out that it must be used with proper controls, reminding us that a technology that facilitates access to justice must be welcomed and given proper controls, not proscribed.28 Openness to AI comes with clear requirements, such as those set out in the opinion published by the Superior Court on October 24, 2023. In the notice, the Superior Court called for caution, the use of reliable sources (court websites, recognized commercial publishers, established public services) and “meaningful human control” of generated content.29 The practice guides issued by various courts all point in the same direction: We should govern the use of AI without banning it. The Federal Court requires a declaration when a filed document contains AI-generated content and insists on “human in the loop” verification.30 The Court of Appeal of Quebec,31 the Court of Québec32 and the municipal courts33 have issued similar warnings: need for caution, authoritative sources, hyperlinks to recognized databases and full responsibility of the author. Nowhere is AI banned—all make its use conditional on verification and traceability. Some clues suggest that the judiciary itself is using artificial intelligence. In the Small Claims Division, on at least two occasions, a judge attached English translations generated by ChatGPT as a courtesy, specifying that they had no legal value and that the French version prevailed.34 In family law, a Superior Court decision in a family matter clearly used a Statistics Canada link identified by an AI tool (the URL includes “utm_source=chatgpt.com”), but the reasoning remains rooted in primary sources and case law: The AI was used as a search tool, not to provide a legal basis.35 A decision handed down on September 3, 2025, by the Commission d’accès à l’information is a particularly good illustration of openness with proper controls. In Breton c. MSSS,36 the court allowed exhibits containing content generated by Gemini and Copilot, because they were corroborated by relevant, primary sources that had already been filed (Journal des débats, newspaper excerpts, official websites). Despite art. 2857 C.C.Q. and the flexibility of administrative law, the Court reiterated that AI content is admissible if, and only if, it is verified, traceable and supported by official sources. AI that aims to please us and that we want to believe Two constants emerge from the sanctioned cases: excessive confidence in the AI’s reliability and underestimated risk of hallucination. In the United States, in Mata v. Avianca,37 the lawyers claimed that they believed that the tool could not invent cases. In Canada, in Hussein v. Canada,38 the plaintiff’s lawyer claims to have relied on an AI service in good faith, without fully realizing that it was necessary to check references. In Australia, in JNE24 v. Minister for Immigration and Citizenship,39 the court reported an over-reliance on tools (Claude, Copilot) and insufficient verification. In Quebec, the AHT found that a lessor had been misled by the use of artificial intelligence,40 while at the Administrative Labour Tribunal (ALT), ChatGPT-generated answers deemed to be approximately 92% accurate were used.41 These examples describe a generalized trust bias that is particularly risky for those who represent themselves: AI is perceived as a reliable way to gain speed, but in reality, it requires greater human control. Large language models are optimized to produce plausible and engaging responses; but, without proper controls, they tend to confirm user expectations rather than pointing out their own limitations.42 A notice published last April by OpenAI concerning an update that made its model “overly supportive” testifies to the underlying complexity of striking the right balance between engagement and preciseness.43 This makes it easier to understand how a quarrelsome litigant may have persuaded himself, based on an AI response, that he was entitled to personally sue a judge for judicial acts perceived as biased.44 Models trained to “please” or to keep users engaged can generate responses that, in the absence of legal contextualization, amplify erroneous or imprudent interpretations. Although AI service providers generally seek to limit their liability for the consequences of incorrect answers, the scope of such clauses is necessarily limited. When ChatGPT, Claude and Gemini apply legal principles to facts reported by a user, doesn’t the entity offering the service expose itself to the rules of public order that make such acts the exclusive prerogative of lawyers, which cannot be waived by a simple disclaimer? In Standing Buffalo Dakota First Nation v. Maurice Law, the Saskatchewan Court of Appeal reiterated that the prohibition on the practise of law applies to any “person" (including a corporation) and expressly contemplated that technological mediation would not change the analysis of what prohibited acts are.45 In Quebec, this principle is enshrined in section 128 of the Act respecting the Barreau du Québec and the Professional Code: general legal information is permitted, but individualized advice can only be provided by a lawyer. While some aberrant situations have involved lawyers, unrepresented claimants or plaintiffs appear to be the most exposed to the effects of AI. Should we focus on educating users first, or restrict certain uses? The tension between access to justice and protecting the public is quite obvious. Conclusion The Specter Aviation ruling confirms that artificial intelligence has its place in court, provided that rigorous controls are applied to it, and that it is useful when verified, but sanctionable when not. While AI offers unprecedented possibilities in terms of access to justice, combining it with public protection remains a major challenge. Despite this clear signal, containing over-reliance on tools designed to be engaging and supportive, and that claim to have an answer to everything, will remain a challenge for years to come. Specter Aviation Limited c. Laprade, 2025 QCCS 3521, online: https://canlii.ca/t/kfp2c Id., paras. [35], [53] Id. para. [43] Id. para. [60] Chicoine c. Vessia, 2023 QCCA 582, https://canlii.ca/t/jx19q, para. [20]; Gagnon c. Audi Canada inc., 2018 QCCS 3128, https://canlii.ca/t/ht3cb, paras. [43]–[48]; Layla Jet Ltd. c. Acass Canada Ltd., 2020 QCCS 667, https://canlii.ca/t/j5nt8, paras. [19]–[26]. Code of Civil Procedure, CQLR, c. C-25.01, arts. 339–341. Chicoine c. Vessia, supra. note 5, paras. [20]–[21]; Constellation Brands US Operations c. Société de vin internationale ltée, 2019 QCCS 3610, https://canlii.ca/t/j251v, paras. [47]–[52]; Webb Electronics Inc. c. RRF Industries Inc., 2023 QCCS 3716, https://canlii.ca/t/k0fq8, paras. [39]–[48]. 9401-0428 Québec inc. c. 9414-8442 Québec inc., 2025 QCCA 1030, https://canlii.ca/t/kdz4h, paras. [82]–[87]; Biron c. 150 Marchand Holdings inc., 2020 QCCA 1537, https://canlii.ca/t/jbnj2, para. [100]; Groupe manufacturier d’ascenseurs Global Tardif inc. c. Société de transport de Montréal, 2023 QCCS 1403, https://canlii.ca/t/jx042, para. [26]. Groupe manufacturier d’ascenseurs Global Tardif inc. c. Société de transport de Montréal, supra. note 8, paras. [58]–[61] ($100,000 to Global Tardif, $60,000 to Intact Assurance, $40,000 to Fujitec, all as legal costs awarded under art. 342 C.C.P.); see also $20,000 for an application for an amendment made on the 6th day of a trial, forcing a continuance: Paradis c. Dupras Ledoux inc., 2024 QCCS 3266, https://canlii.ca/t/k6q26, paras. [154]–[171]; Webb Electronics Inc. c. RRF Industries Inc., supra. note 7. Layla Jet Ltd. c. Acass Canada Ltd, supra note 5, paras. [23]–[28]. Électro-peintres du Québec inc. c. 2744-3563 Québec inc., 2023 QCCS 1819, https://canlii.ca/t/jxfn0, paras. [18]–[22], [35]–[38]; see also Constant c. Larouche, 2020 QCCS 2963, https://canlii.ca/t/j9rwt, paras. [37]–[40] (repeated delays in adhering to undertakings despite an order, sanction: $5,000). Constellation Brands US Operations c. Société de vin internationale ltée, supra. note 7, paras. [39]–[43], [47]–[52]; see also AE Services et technologies inc. c. Foraction inc. (Ville de Sainte-Catherine), 2024 QCCS 242, https://canlii.ca/t/k2jvm (repeated delays in transmitting promised documentation and breach of an undertaking before the court; compensation of $3,000). Gagnon c. SkiBromont.com, 2024 QCCS 3246, https://canlii.ca/t/k6mzz, paras. [29]–[37], [41]. J.R.V. v. N.L.V., 2025 BCSC 1137, https://canlii.ca/t/kcsnc, paras. [51]–[55]. Hussein v. Canada (IRCC), 2025 FC 1138, https://canlii.ca/t/kctz0, paras. [15]–[17], applying Kuehne + Nagel Inc. v. Harman Inc, 2021 FC 26, https://canlii.ca/t/jd4j6, paras. [52]–[55] (reiterating the principles of Young v. Young and the two-step test: (1) conduct causing costs to be incurred; (2) discretionary decision to impose costs personally). AQ v. BW, 2025 BCCRT 907, https://canlii.ca/t/kd08x, paras. [15]–[16], [38]–[40]. Lloyd's Register Canada Ltd. v. Choi, 2025 FC 1233, https://canlii.ca/t/kd4w2 Mata v. Avianca, Inc, No. 22-cv-1461 (PKC) (S.D.N.Y. June 22, 2023) (sanctions order), online: Justia https://law.justia.com/cases/federal/district-courts/new-york/nysdce/1:2022cv01461/575368/54/ Tribunale di Latina (giud. Valentina Avarello), sentenza 23 septembre 2025, Atto redatto con intelligenza artificiale a stampone, con scarsa qualità e mancanza di pertinenza: sì alla condanna ex art. 96 c.p.c., La Nuova Procedura Civile (september 29, 2025), online: https://www.lanuovaproceduracivile.com/atto-redatto-con-intelligenza-artificiale-a-stampone-con-scarsa-qualita-e-mancanza-di-pertinenza-si-alla-condanna-ex-art-96-c-p-c-dice-tribunale-di-latina/ Australia, Federal Circuit and Family Court of Australia (Division 2), JNE24 v. Minister for Immigration and Citizenship, [2025] FedCFamC2G 1314 (August 15, 2025), Gerrard J, online: AustLII https://www.austlii.edu.au/cgi-bin/viewdoc/au/cases/cth/FedCFamC2G/2025/1314.html United States, District Court for the Eastern District of Oklahoma, Mattox v. Product Innovations Research, LLC d/b/a Sunevolutions; Cosway Company, Inc.; and John Does 1–3, No. 6:24-cv-235-JAR, Order (October 22, 2025), online: Eastern District of Oklahoma https://websitedc.s3.amazonaws.com/documents/Mattox_v._Product_Innovations_Research_USA_22_October_2025.pdf Régie du bâtiment du Québec c. 9308-2469 Québec inc. (Éco résidentiel), 2025 QCRBQ 86, online: https://canlii.ca/t/kfdfg, paras. [159]–[167]. Blinds to Go Inc. c. Blachley, 2025 QCCS 3190, online: https://canlii.ca/t/kf963 para. [57] and n. 22. Lozano González c. Roberge, 2025 QCTAL 15786, online: https://canlii.ca/t/kc2w9 paras. [7], [17]–[19]. Marna c. BKS Properties Ltd, 2025 QCTAL 34103, online: https://canlii.ca/t/kfq8n paras. [18], [21]–[25]; Campbell c. Marna, 2025 QCTAL 34105, online: https://canlii.ca/t/kfq81 paras. [18], [21]–[25]. Morrissette c. R., 2023 QCCQ 12018, online: https://canlii.ca/t/k3x5j para. [43]. Léonard c. Agence du revenu du Québec, 2025 QCCQ 2599, online: https://canlii.ca/t/kcxsb paras. [58]–[64]. Specter Aviation Limited c. Laprade, supra. note 1, para. [46]. Superior Court of Quebec, “Notice to Profession and Public – Integrity of Court Submissions When Using Large Language Models,” October 24, 2023, online: https://coursuperieureduquebec.ca/fileadmin/cour-superieure/Districts_judiciaires/Division_Montreal/Communiques/Avis_a_la_Communite_juridique-Utilisation_intelligence_artificielle_EN_October_24_2023.pdf Federal Court, “Notice to the Parties and the Profession – The Use of Artificial Intelligence in Court Proceedings,” December 20, 2023, online: https://www.fct-cf.ca/Content/assets/pdf/base/2023-12-20-notice-use-of-ai-in-court-proceedings.pdf; Federal Court, Update – The Use of Artificial Intelligence in Court Proceedings, May 7, 2024, online: https://www.fct-cf.ca/Content/assets/pdf/base/FC-Updated-AI-Notice-EN.pdf Court of Appeal of Quebec, “Notice Respecting the Use of Artificial Intelligence Before the Court of Appeal”, August 8, 2024, online: https://courdappelduquebec.ca/fileadmin/dossiers_civils/avis_et_formulaires/eng/avis_utilisation_intelligence_articielle_ENG.pdf Court of Québec, “Notice to the legal community and the public – Maintaining the integrity of submissions before the Court when using large language models,” January 26, 2024, online: https://courduquebec.ca/fileadmin/cour-du-quebec/centre-de-documentation/toutes-les-chambres/en/NoticeIntegriteObservationsCQ_LLM_en.pdf Cours municipales du Québec, Avis à la profession et au public – Maintenir l’intégrité des observations à la Cour lors de l’utilisation de grands modèles de langage, December 18, 2023, online: https://coursmunicipales.ca/fileadmin/cours_municipales_du_quebec/pdf/Document_d_information/CoursMun_AvisIntegriteObservations.pdf Bricault c. Rize Bikes Inc., 2024 QCCQ 609, online: https://canlii.ca/t/k3lcd n. 1; Brett c. 9187-7654 Québec inc, 2023 QCCQ 8520, online : https://canlii.ca/t/k1dpr, n. 1. Droit de la famille – 251297, 2025 QCCS 3187, online: https://canlii.ca/t/kf96f paras. [138]–[141]. Breton c. Ministère de la Santé et des Services sociaux, 2025 QCCAI 280, online: https://canlii.ca/t/kftlz, paras. [24]–[26], [31]. Mata v. Avianca, Inc, supra note 18. Hussein v. Canada (IRCC), 2025 FC 1138, supra note 15, paras. [15]–[17]. JNE24 v. Minister for Immigration and Citizenship, supra note 20. Lozano González c. Roberge, supra note 24, para. [17]. Pâtisseries Jessica inc. et Chen, 2024 QCTAT 1519, online: https://canlii.ca/t/k4f96, paras. [34]–[36]. See Emilio Ferrara, “Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models” (2023), SSRN 4627814, online: https://doi.org/10.2139/ssrn.4627814; Isabel O. Gallegos et al, “Bias and Fairness in Large Language Models: A Survey” (2024) 50:3 Computational Linguistics 1097, doi: 10.1162/coli_a_00524. See OpenAI, “Sycophancy in GPT-4o: what happened and what we're doing about it,” April 29, 2025, online: https://openai.com/research/sycophancy-in-gpt-4o; see also “Expanding on what we missed with sycophancy,” May 2, 2025, online: https://openai.com/index/expanding-on-sycophancy/ [44]Verreault c. Gagnon, 2023 QCCS 4922, online: https://canlii.ca/t/k243v, paras. [16], [28]. Standing Buffalo Dakota First Nation v. Maurice Law Barristers and Solicitors (Ron S. Maurice Professional Corporation), 2024 SKCA 14, online: https://canlii.ca/t/k2wn9 paras. [37]–[40], [88]–[103].

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