Artificial Intelligence

Artificial intelligence (AI) is no longer merely a futuristic concept but a present-day reality—a practical business tool that is routinely used in management and production. Organizations are increasingly adopting generative AI and analytics solutions for tasks such as writing, sorting, decision-making, monitoring and evaluating. However, this is often done without any prior structured planning.  Employers now face the dual challenge of achieving productivity gains quickly while ensuring that AI does not pose legal, reputational or operational risks. The range of applications is expanding to include writing assistance, decision-making support, performance analysis, digital monitoring and incident and accident prediction. This raises questions of interest to both executives and the media. For example, who should be held responsible when the tool makes a mistake? What data is being used? How far can employers go in monitoring their employees?  If properly regulated, AI can support innovation and expedite its implementation, while also contributing to workplace well-being, health and safety. In Quebec, these benefits are particularly significant in the context of an aging population, labour shortages and increased pressure to boost productivity. Addressing this situation requires solutions that foster growth and competitiveness.  The use of AI systems in the workplace raises real and multifaceted challenges. These include protecting personal information and maintaining confidentiality, establishing liability and accountability where errors or failures occur, and considering the potential impact on workloads and the work environment.  Aware of the effect that digital transformation and AI are having on the workplace, the Minister of Labour launched a consultative process to assess whether existing legislation adequately addressed these developments. In October 2025, he tasked the Comité consultatif du travail et de la main-d’œuvre (the “CCTM”) with further exploring ideas and developing a shared vision regarding:  Consultation processes that factor in the implications of AI use in the workplace;  Ethical, governance and transparency principles in human resources decision-making;  The prevention of emerging occupational health and safety risks.1  CCTM report: Recommendations  The Avis du CCTM concernant les enjeux entourant l’implantation et l’usage des systèmes d’intelligence artificielle en milieux de travail au Québec [CCTM report on the implications of implementing and using artificial intelligence systems in Quebec workplaces] (“CCTM Report”) was released on April 29, 2026. The recommendations set out in the report regarding the implementation and use of AI systems in Quebec workplaces include, in particular:  Applying the current legal framework governing labour and employment law in Quebec; Preserving the essential role of human judgment—and the responsibility that comes with it—in automated decision-making processes;   Applying laws respecting the protection of personal information2 when developing, implementing and using AI systems, and prioritizing solutions that limit the use of electronic surveillance;  Implementing an algorithmic impact assessment process that would involve employees and take into account the impact of automated decision-making on fundamental rights and privacy;  Ensuring that organizations continue to support employee mobility and ongoing professional development;  Regulating algorithms through sound governance practices that promote transparency and explainability in algorithmic decision-making;  Placing emphasis on the need to pay special attention to discriminatory biases.   In its report, the CCTM also makes recommendations to the Commission d’accès à l’information, such as updating its guide on drafting privacy policies to include disclosure requirements regarding AI and surveillance technologies, and encouraging employers to inform employees of their intention to use partially or fully automated decision-making processes.  Lastly, the CCTM recommends that the Ministère du Travail develop, in collaboration with the CCTM, a guide to support the implementation of responsible, compliant and socially acceptable AI.  Guide released by the Ministère du Travail: 5 principles  Further to the recommendations made in the CCTM report, the Minister of Labour released, on June 12, 2026, a document titled L’intelligence artificielle au travail : Guide d’accompagnement pour une intégration responsable [A guide to using artificial intelligence responsibly in the workplace] (the “Guide”).  The Guide aims to ensure that AI is integrated into workplaces in a responsible, user-centric and collaborative manner. It sets out five (5) key principles designed to inform discussions and help organizations recognize the main issues, namely:  Respect for rights and freedoms in the workplace;  Protection of privacy and data governance;  Governance, participation and social dialogue;  Human oversight and transparency;  Sustainable development and well-being.  For each principle, the Guide provides examples of how AI is used in the workplace. It also highlights the associated benefits and challenges, and suggests practical steps to ensure that AI is adopted and used responsibly.  The Guide serves as a practical, evolving tool that organizations and labour market stakeholders are encouraged to tailor to their specific circumstances.   Accessible and regulated AI  While the adoption of AI in the workplace offers tangible opportunities for improvement, it also raises important issues that require careful oversight. With that in mind, the Guide aims to support the use of AI in a way that protects rights and users, while taking workplace considerations into account. It also aims to provide the various stakeholders involved with the tools they need to facilitate the adoption of AI at work.  A number of interesting challenges are likely to arise over the next few years, and Lavery’s highly qualified professionals are ready to help you deal with them. Contact the team today.  Comité consultatif du travail et de la main-d’œuvre, Avis du CCTM concernant les enjeux entourant l’implantation et l’usage des systèmes d’intelligence artificielle en milieux de travail au Québec, online: lien, April 9, 2026, p. 6. (In French only) Act respecting the protection of personal information in the private sector, CQLR c. P-39.1; Act respecting Access to documents held by public bodies and the Protection of personal information, CQLR c. A-2.1.

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.

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.

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