In the summer of 2023, I found myself in a room with security researchers, ethicists, and engineers, tasked with a peculiar assignment: make an advanced AI system fail. Our objective was to probe a large language model for vulnerabilities, to elicit harmful outputs, expose hidden biases, and stress-test the guardrails its creators had painstakingly constructed. This practice, known as , has become the crucible through which responsible AI developers assess whether their systems are fit for public deployment.

Although I am not a computer scientist, my work as a law professor and legal scholar has long engaged with the capacity of legal frameworks to respond to technological disruption. In that room, surrounded by experts who could articulate attention mechanisms and tokenisation with casual fluency, it was evident that I had as much to learn as I had to offer. At the same time, the exchange highlighted that legal training contributes a distinct and necessary perspective. Lawyers are trained to identify risk, anticipate misuse, evaluate harm, and analyze the real-world consequences that may flow from algorithmic failures. The experience therefore underscored a broader insight, one that the AI industry has perhaps been slow to recognise: that effective red teaming exercises need more lawyers.

The Evolution of Adversarial Testing

Red teaming traces its lineage to Cold War military strategy, where designated adversaries (the “red team”) would simulate enemy attacks to expose defensive weaknesses. The practice migrated to in the 1990s, where penetration testers probed corporate networks before malicious hackers could. When applied to AI systems, red teaming evolved from what hobbyists once called “jailbreaking” (the sport of coaxing chatbots into misbehaviour) into a endorsed by governments and embedded in emerging regulatory frameworks.

Today, AI red teams attempt to elicit dangerous capabilities: the generation of misinformation, the production of discriminatory content, the leakage of private training data, or the provision of instructions for genuine harm. The widely discussed and the , both identify adversarial testing as essential to responsible development. Major AI labs now routinely engage external experts to probe their systems before release, a practice that has become standard (even obligatory) in the field.

Yet red teaming is not without limitations. Exercises are necessarily time-bound; testers cannot anticipate every deployment context or user intention. Findings do not always translate into engineering solutions. And crucially, red teams often lack the interdisciplinary breadth necessary to identify the full spectrum of potential harms. A team composed primarily of computer scientists and ethicists will see certain vulnerabilities clearly and remain blind to others entirely.

What Legal Eyes See

During our red team exercise, while my colleagues focused generally on whether a model would generate violent content or produce biased outputs, my attention gravitated toward a different set of questions. When the model confidently asserted false statements about a living person, I immediately thought of : the constituent elements of the tort, variations across jurisdictions, and the practical difficulty of establishing fault when the “speaker” is an algorithmic system. Similarly, when we successfully extracted fragments of what appeared to be training data, my focus shifted to data protection law, in particular the , and the question of whether such extraction might amount to a violation of data protection obligations.

These were not simple peripheral concerns. They were legally cognisable harms, with potential implications for regulatory scrutiny, reputational damage, and liability exposure. Yet without legal expertise in the room, they might have been catalogued simply as “problematic outputs” rather than understood in their full juridical dimension. This experience thus illuminated why lawyers should be integral members of red teams, not as afterthoughts or compliance consultants, but as core participants in the adversarial imagination that safety testing demands.

Five Reasons to Invite Lawyers to Break Your AI

1. Lawyers understand harm with juridical precision

To begin with, legal training brings a distinctive precision to the identification and classification of harm. The law does not treat all harms equally. Defamation, discrimination, privacy violations, and intellectual property infringement each carry specific definitions, evidentiary burdens, and remedial consequences. When a model produces troubling outputs, lawyers can distinguish between the merely objectionable and the actionable, a distinction that fundamentally shapes risk assessment and mitigation priorities. The in the United States illustrates how model failures can rapidly become legal crises.

2. Privacy law requires specialised fluency

A related consideration arises in the domain of privacy, where legal analysis demands specialised fluency. The global patchwork of data protection regimes, the in Europe, the in California, , , and Kenya’s , have created intricate and sometimes contradictory obligations. When red teamers probe for memorization of personal data or attempt to extract training data, lawyers are equipped to assess whether such vulnerabilities implicate specific regulatory requirements and to anticipate how data protection authorities may respond.

3. Lawyers are professionally trained in adversarial reasoning

Equally important is the legal profession’s grounding in adversarial reasoning. The courtroom is, in essence, an institutionalised red team: opposing counsel systematically probes weaknesses in argument, inconsistencies in evidence, and ambiguities in position. This mode of reasoning cultivates a particular cognitive disposition-the habit of thinking from the perspective of an adversary-that translates directly to the work of identifying how malicious actors might exploit AI vulnerabilities. Lawyers are trained, quite literally, to find the holes and the points of failure

4. Legal expertise enables regulatory foresight

The AI governance landscape is shifting rapidly. The imposes binding obligations on high-risk systems. Numerous . Countries across Africa, from to , have developed national AI strategies. Lawyers can stress-test systems against forthcoming requirements, helping developers prepare for regulatory futures rather than merely react to regulatory presents.

5. Lawyers navigate cross-jurisdictional complexity with native facility

A model deployed globally must contend with the legal frameworks of dozens of jurisdictions simultaneously. What constitutes prohibited hate speech in differs from what triggers liability in the United States, which differs again from prohibitions in or . Lawyers versed in comparative and international law can map how a single model output might cascade into disparate legal consequences across borders, a complexity that homogeneous technical teams are ill-equipped to navigate.

An Invitation and an Imperative

To fellow lawyers who have watched the AI revolution from the side lines, uncertain whether our expertise has purchase in this domain: the answer is that it does. The skills we have cultivated through legal training (close reading, adversarial thinking, the anticipation of misuse, the parsing of ambiguity) are precisely what red teaming demands. I don’t think we need to fully understand the mathematics of transformer architectures to recognise that a model’s confident fabrications might constitute negligent misrepresentation, or that its profiling capabilities might violate privacy law or raise constitutional questions under . Legal analysis operates at the level of consequences, responsibility, and rights, precisely where many AI risks ultimately materialise.

To AI developers and safety organisations: we need diversified red teams. The homogeneity that characterises many current exercises (dominated by security researchers and machine learning engineers) leaves some critical vulnerabilities unexamined. Legal scholars, practising attorneys, and even law students possess perspectives that will reveal risks invisible to those who share similar training and assumptions.

The stakes warrant this expansion. As AI systems assume greater roles in consequential domains (employment, credit, criminal justice, healthcare, content moderation), the potential for legally significant harm multiplies. I think that red teaming that excludes legal expertise is red teaming that leaves the courtroom door unguarded. In my own journey from legal academic to participant in AI safety exercises, I have learned that, sometimes, the most valuable insights emerge from disciplinary friction. Questions that may initially appear naive to engineers can illuminate genuine legal and societal risks. This is why we need more of that productive dissonance. We need more lawyers willing to tackle the machines.

is a Vanier Scholar and Ph.D. candidate at Osgoode Hall Law School, examining ways that the legitimization of AI is impacting the pursuit and realization of human rights in Africa.

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"Thank you, Chair, and members of this committee. My name is Carys Craig. I am a professor at Osgoode Hall Law School, 91��ɫ, where my research focuses on copyright, technology, and the public interest. I submitted briefs to the Government’s previous consultations on AI and copyright, and I’ve published widely on the topic. I appreciate the opportunity to share my views today.

I want to make three points about copyright protection relevant to this Committee’s work. First, it is vital to distinguish copyright law from AI regulation; copyright is the wrong legal tool for regulating AI technologies. Second, relatedly, copyright law must not obstruct AI research, development, and training in Canada. And third, Canada’s copyright law must continue to refuse copyright protection to AI-generated works.

Copyright Is the Wrong Tool for AI Regulation

There is understandable concern about generative AI —its effects on creative workers, cultural industries, information integrity, the concentration of foreign corporate power. I share those concerns. But I urge the committee to be cautious about including expanded copyright protections as part of an AI regulatory package to address them. Copyright exists to encourage the creation and dissemination of works, to reward authors and foster a vibrant public domain. It is technology neutral. It is not designed to govern technology risks or restrain technological developments; it should not be pressed into that service now.

The real risks of AI — from bias and misinformation to deepfakes and privacy violations, labour displacement, corporate consolidation — demand dedicated, fit-for-purpose regulatory responses. Expanding copyright control risks distorting copyright principles while failing to address—or indeed worsening—the harms themselves. This is what I’ve called running into the “AI-Copyright Trap”—mistakenly turning to copyright as an easy catch-all (or for some, a windfall) in response to the threats posed by generative AI.

Copyright Law Must Not Obstruct AI Training

My second point concerns AI training. Some have called for mandatory licensing for copyrighted works used in training data, backstopped by owners’ rights to opt out or in. I understand the impulse, but the consequences of this approach would be deeply harmful.

Under current law, it is not established and far from clear that training AI on copyright works even implicates the rights of owners. When an AI system is trained, it does not memorize or reproduce the works it processes. It translates expressive content into statistical patterns — meaning becomes math. This is a technical, intermediate, non-public use to extract information that copyright does not protect. But even if copyright extended to this data extraction process, most text and data mining is likely lawful without permission or license under Canada's fair dealing provisions, as interpreted by the Supreme Court. If the Committee is interested in supporting AI research and innovation in Canada, the real problem is the current legal uncertainty, not illegality.

Requiring licensing would create a pay-to-play system regulated by private actors. Only the wealthiest corporations could afford access to the vast data troves required. Academic researchers, non-profits, startups and SMEs will be shut out. This would concentrate AI development further in the hands of Big Tech — the very outcome many are trying to prevent. It would also incentivize secrecy, reduce the diversity of AI systems and competition, exacerbate bias, and be practically impossible to administer effectively (as implementation efforts in the EU reveal). It would surely off-shore AI development while Canadian creators would gain little if anything. [Canada’s focus should be on public data governance, not shoring up its private commodification.]

So, if Canada is to amend the Copyright Act, it should be to confirm that text and data mining for informational analysis does not constitute infringement. This was the original INDU recommendation in the 2019 Copyright Act Review, and it remains the best way to support a healthy AI ecosystem in Canada. It would align with emerging US fair use jurisprudence—but give us the significant advantage of legal clarity.

Refuse Copyright Protection for AI-Generated Outputs

My third and final point concerns AI outputs. The most effective thing copyright can do to protect human creators is to maintain the position that copyright requires a human author — [a work must originate from a human being to attract protection] while AI-generated content belongs in the public domain. That is the correct result, and it protects the role of human creators in the creative industries. Granting copyright or new rights in AI-outputs would be an unnecessary, misplaced incentive and further chill human creativity.

Conclusion

In closing, I want to emphasize that copyright law, at its best, serves human creativity and the public interest. It exists because we value what human beings create, share, and learn from each other. We cannot allow it to become a tool for controlling technology, or a bargaining chip for corporate licensing deals and rent-seeking, or a vehicle for granting monopoly rights over information or machine-generated content. I urge the committee to keep copyright’s principled limits and practical effects in view — there are many more apt solutions to the risks posed by AI systems. Thank you."

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Innovation and technology conversations are almost always oriented toward what comes next, what lies ahead, what will disrupt, or what will transform. Intellectual property law sits at the centre of these forward-looking debates, shaping how innovation is protected, disrupted, and governed. Yet the conversation at the launch of , hosted recently by IP Osgoode, suggested a more compelling perspective: looking towards IP futures does more than help us anticipate change; it helps us see the present more clearly.  Artificial intelligence, digital globalization, and shifting economic models are not only reshaping innovation—they are also illuminating the blind spots embedded within todays IP structures. These blind spots surface in unresolved questions about originality and human authorship, the erosion of local normative priorities within global trade flows, and the largely invisible human labour underlying IP and information technologies.

, co-editor of the volume, opened the event by emphasizing the importance of emerging scholars in shaping the vitality of IP research. Reynolds, alongside co-editors and , has brought together an impressive collection of 18 chapters by 23 authors who participated in the 2023 workshop hosted at UBC's Allard School of Law. The resulting volume spans five thematic areas: the future of international IP treaties; evolving questions in Canadian law and artificial intelligence (AI); the relationship between Indigenous legal traditions and colonial IP frameworks; the future of IP in a digital and global environment; and the ways in which IP law can both reinforce and mitigate inequality. Together, these themes underscore that IP law is not merely a technical regulatory tool, it is a place where economic priorities, social justice concerns, and cultural values converge. Viewed through a future-oriented lens, each of these domains reveals pressures already reshaping the doctrinal foundations of IP.

Originality, Human Authorship, and the Limits of Existing Doctrine

Innovation debates are oriented toward what comes next, and questions of authorship reveal how unprepared existing doctrines may be. The rapid rise of AI-generated content forces copyright law to confront assumptions long taken for granted: that creativity is human, that authorship is identifiable, and that originality reflects individual intellectual effort. In his presentation, “Protection of AI-Generated Images in Canadian Copyright Law,” examined whether such works can satisfy the originality requirement, suggesting that protection may arise only where users provide sufficiently detailed inputs and where AI systems faithfully execute those executions -conditions that are rarely satisfied in practice. Most users do not invest the level of skill and judgment required to craft detailed prompts, and the outputs of AI black box systems often deviate from what the user intended. Dr. Rei-Anderson illustrated this point by referencing models’ tendency to hallucinate when asked to produce precise outputs or maintain specific spatial relationships.

[*The image below is Chat-GPT's output when asked, “What would it look like if I looked outside an acoustic guitar?” The fingerboard, strings, bridge, and saddle are all incorrectly positioned.]

Recognizing the gaps between expressive intention and AI-generated outputs highlights a deeper theme: the originality doctrine may have to adapt as technologies evolve if copyright law is to preserve a normative commitment human intellectual effort.

Viewed through a future-oriented lens, this is not simply a novel doctrinal puzzle. It exposes a present blind spot. When outputs emerge from opaque systems blending prompts, training data, and algorithmic processes, authorship becomes diffuse and originality difficult to anchor. The debate over AI-generated works therefore reveals an existing tension: doctrines built around individual human creativity struggle to accommodate collaborative, machine-mediated forms of production. The future of authorship, in this sense, illuminates the fragility of its present foundations.  

Globalization and the Disappearance of Local Normative Priorities

’s presentation shifted the discussion to the tension between the territorial nature of IP law and private international law (PIL), a dynamic that reveals another blind spot within IP law: the erosion of local normative priorities. Territoriality in IP law, she noted, is not merely technical, rather, it represents a manifestation of a state’s sovereign authority to protect fundamental national policies, including freedom of expression, education, and access to knowledge. These policies reflect deeply embedded social and economic values, and IP law often serves as a vehicle for their expression.

However, territoriality increasingly conflicts with the goals of PIL, which seeks to reduce multi-jurisdictional litigation and improve efficiency across borders. Daniel introduced the concept of “ICE Bias” (Initiative, Choice, and Enforcement) to describe the systemic imbalance between right holders and users in cross-border disputes. Right holders typically initiate proceedings, select favorable forums, and possess stronger incentives to enforce judgments, while users are often limited to seeking declaratory relief.

This imbalance encourages strategic litigation, where rights holders obtain judgments in restrictive jurisdictions and attempt to enforce them globally. Through cases such as and ., Daniel illustrated how global injunctions risk undermining domestic policy decisions. Her concern reflects a broader question about the future of IP in a globalized environment: how can law remain responsive to local values while operating within transnational networks?

In this manner, globalization does not merely generate efficiency, it risks flattening normative diversity. When enforcement mechanisms privilege scalability and uniformity, local concerns can be overshadowed by global market interests. Daniel proposed the development of specialized IP forums to complement general PIL venues, thereby preserving territorial policy choices while managing cross-border disputes.

Labor Exploitation in AI Training Processes

While AI is often framed as an autonomous technological force, Professor Teshager Dagne redirected attention to the human labor embedded within AI systems, emphasizing that data labeling, preprocessing, and model training depend on workers in economically vulnerable regions whose contributions remail largely invisible within IP frameworks.

Drawing on research conducted in Uganda and Ethiopia, Dagne described environments resembling digital “sweatshops,” where workers labeled data behind laptops rather than operating sewing machines. Although copyright doctrine recognizes human skill and contribution, data-labeling labor often falls outside traditional authorship frameworks. Dagne suggested joint ownership as a potential remedy, but acknowledged doctrinal and practical barriers, particularly where workers have signed away rights. This gap reflects a broader policy deficiency and highlights the need for statutory reform.

Dagne’s intervention emphasizes the socio-economic values embedded within legal systems: IP law does not merely regulate innovation; it shapes the distribution of benefits and burdens. A future-oriented IP perspective highlights that fairness in IP cannot be assessed only at the level of ownership or protection, it must account for the full technological pipeline and the conditions under which knowledge and data are produced.

Seeing the Present Through IP Futures

The launch of Intellectual Property Futures: Exploring the Global Landscape of IP Law and Policy does more than anticipate technological change; it provides an opportunity to identify and critically examine the blind spots embedded within the contemporary IP legal landscape. Looking ahead, in other words, becomes a way of more clearly seeing the problems with the present – and this wide-ranging, open-access collection provides us with an excellent vantage point to do just that.  

Xiang Zhang is a doctoral student at Osgoode Hall Law School and a Senior IP Osgoode Research Fellow, with a strong interest in advancing open-source and open access to knowledge.

Dominic Rochon is a 2L J.D. student at Osgoode Hall Law School and an IP Osgoode Research Fellow, with an interest in music, copyright, and user-generated content.

16 April 2026

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In cardiac medicine, the condition of the heart cannot be assessed in a state of rest, nor is it wise to wait for moments of acute failure. Clinicians instead employ stress testing to measure cardiac performance under conditions of controlled exertion, typically by monitoring patients as they walk or run on a treadmill. This diagnostic technique often reveals vulnerabilities undetectable at rest.  Under strain, hidden weaknesses surface—irregular rhythms, constricted pathways, structural fragilities that calm conditions conceal.

At the recent panel, “,” hosted at Osgoode by Professor , one theme pulsed beneath every doctrinal discussion: artificial intelligence is placing the law under sustained pressure. And if law is the heart of a democratic society - circulating norms, distributing responsibility, sustaining trust - then AI functions as the stressor that tests its endurance in the face of emerging and increasingly complex legal challenges. 

AI systems now influence hiring, lending, securities trading, education, communications, and health care. They make or shape decisions once exclusively human; and when harm occurs (economic loss, discrimination, psychological injury, market manipulation), the causal chains are longer, more opaque, and more distributed across multiple actors and systems. In this context, the question becomes whether the law can preserve its steady cadence as the pace of technological change pushes it to run faster and faster.

For centuries, the common law has evolved incrementally, adapting to industrialization, mass production, financialization, and digitization. Negligence, fraud, public nuisance, product liability - these doctrines were built to regulate human actors making human decisions. At the same time, the legal system has embedded adaptive principles that enable it to evolve—doctrines such as technological neutrality, incrementalism through analogy, the “living tree” doctrine, and the open-textured character of standards such as reasonableness and foreseeability. Duties of care are established through proximity. Fault turns on knowledge and intent. Causation links conduct to harm. Liability is allocated. These principles have allowed the law to endure and respond to social and technological change. Artificial intelligence, however, introduced pressures of a different magnitude and speed. It was precisely this tension between doctrinal continuity and technological acceleration that animated the panel’s discussion, which examined how negligence, fraud, and the allocation of responsibility are each being placed under strain in distinct but interconnected ways.

Negligence Under Strain

One of the most vivid examples discussed by the panel was brought by Ontario school boards (including the Toronto District School Board), against Meta Platforms, TikTok, and Snap Inc. Their claim alleges negligent design and public nuisance, that these companies knowingly engineered platforms in ways that caused widespread disruption of the education system – diverting resources, exacerbating behavioural issues, and contributing to mental health crises.

The legal framework invoked in the litigation is not novel. The plaintiffs ground their claim in established negligence principles traceable to : duty of care, breach of the applicable standard, causation, and damages. But here is where the incline steepens, the doctrinal structure is familiar, the difficulty only arises in its application. The harm alleged is primarily relational and economic in nature. Canadian law has historically been cautious about pure economic loss. Establishing a duty therefore requires demonstrating sufficient proximity, and any extension of liability must be justified as an incremental development grounded in considerations of justice and fairness. The central issue is whether the relationship between social media platform designers and educational institutions is sufficiently close to ground such a duty. The negligence doctrine is thus being tested in a new context.

The negligence claims against social media companies illustrate how familiar doctrinal elements encounter novel factual configurations. The plaintiffs do not allege that defendants erected physical barriers to education. Rather, they contend that the platforms were deliberately and knowingly engineered - through geolocation, engagement optimization, and targeted notifications – in ways that foreseeably disrupted the educational system. The defence contests both proximity and foreseeability, arguing that the relationship between platform designers and educational institutions is too attenuated to ground a duty of care. Under these pressures, the courts must decide whether established negligence principles can coherently accommodate systemic harms arising from algorithmic design.

Fraud, Intent, and the Autonomy Problem

Where the social media litigation is testing the elasticity of proximity and foreseeability with negligence doctrine, the emergence of autonomous and learning AI systems places even more profound pressure on the mental-state architecture that underpins fraud.  Traditional fraud requires scienter: knowledge, recklessness, and intent to deceive. A plaintiff must establish a material misstatement, reasonable reliance, and causation. These elements presuppose a human decision-maker capable of forming a culpable state of mind. The difficulty arises when the operative “actor” is an algorithm that develops strategies through machine learning rather than explicit human instruction.

Panelists and considered scenarios involving high-frequency trading systems that might manipulate markets while pursuing a generic instruction such as “maximizing profit.” If no individual human formed a fraudulent intent in the classical sense, can we still allocate liability? Here, the stress test becomes acute. Fraud doctrine was built around human mental states. AI systems destabilize that foundation by generating outcomes that may be strategically sophisticated yet not traceable to a single, conscious mental state. In response, the panel considered several adaptive pathways: imputing an AI system’s “knowledge” to its developers or deployers; analogizing AI to an employee under agency principles; adopting burden-shifting or quasi-strict liability approaches; or relaxing reliance on scienter requirements, as has occurred in certain areas of U.S. securities regulation.  

These proposals do not abandon the doctrinal core. Rather, they attempt to recalibrate its rhythm. The deeper inquiry is whether intent and knowledge must be reconceptualized in functional terms—perhaps by asking whether the AI system would have behaved differently had a consequence been removed (a counterfactual test for “intent”), or whether it used specific information for a defined objective (a scope notion of “knowledge”). The law has historically adjusted its doctrines under technological pressure. The open question is whether the adaptation required here will remain incremental, or whether the strain risks distorting foundational concepts beyond recognition.  

Control, Foreseeability, and the Circulation of Responsibility

Building on the panel’s examination of negligent design claims and the challenges of proving scienter in algorithmic fraud, a further axis of strain emerged in the discussion: how to allocate legal responsibility in AI systems characterized by distributed and fragmented control. The allocation of legal responsibility has long been structured around the concept of control; agentic AI disrupts this conventional alignment. Large language model-based agents can iteratively plan, access external tools, access databases, refine strategies, and execute tasks with minimal ongoing human oversight. A user provides a broad instruction, and the system determines the operational pathway. When harms result, the attribution of control becomes contestable. Is responsibility property assigned to the user who articulated the goal, the developer who designed the model architecture, the corporation that deployed the system, or some distributed network of actors who collectively shaped its training data, parameters, and deployment context? The difficulty is not merely practical but conceptual.

As the incline increases with complexity, foreseeability becomes more and more blurred. Developers may possess systemic knowledge of general risks while lacking foresight into specific outputs. Users control initiation, but not the path taken. Corporations manage deployment but may not anticipate emergent behaviours. This diffusion of control does not mean responsibility disappears. Rather, it strains the arteries through which liability traditionally flows.

Litigation as Emergency Response

As panelist observed, litigation is inherently reactive. By the time a case reaches court, the harm has already materialized. A stress test, by contrast, is diagnostic: it identifies vulnerabilities before catastrophic failure.  When the heart falters under exertion, clinicians intervene (perhaps with medication, with surgery, or with lifestyle change). Similarly, the legal responses to AI cannot be singular. The panelists outlined a list of options. Ex post liability regimes (negligence, nuisance, fraud) will continue to evolve incrementally through judicial interpretation. Ex ante regulatory frameworks may articulate risk tiers or sector-specific obligations. Insurers may function as soft regulators, pricing AI-related risk and influencing corporate behaviour through coverage conditions. Gatekeepers (i.e. auditors, platforms, financial institutions) may assume greater responsibility in monitoring systemic risk.

Evidence of strain does not necessitate doctrinal abandonment. The discussions of the panel suggest that the legal system has not reached doctrinal failure. Negligence law remains operational, even as it confronts novel factual matrices. Fraud doctrine exhibits strain but retains conceptual elasticity. Public nuisance and product liability continue being extended incrementally, grounded in established principles of proximity, knowledge, and control. Nevertheless, the pressures on these doctrines are intensifying, and their continued adaptive resilience cannot be taken for granted.

AI systems scale rapidly. The harms they generate may be diffuse, cumulative, and distributed across institutional and geographic boundaries. Their evidentiary opacity complicates discovery and proof. At the same time, powerful economic incentives drive their deployment and expansion. The heart of the law continues to beat—but at a much faster pace. The real risk is not that AI requires immediate, sweeping abandonment of existing doctrine. Rather, it is that we fail to attend to the diagnostic signals now emerging.  Where the law shows early signs of strain (evidentiary bottlenecks, liability gaps, doctrinal contortions), we must respond with thoughtful regulatory design—not complacency.

AI does not introduce entirely new moral dilemmas. Rather, it accelerates, amplifies, and exacerbates existing ones. The discomfort of the present moment reveals where our legal assumptions about agency, causation, and responsibility may require attention and refinement. If law functions as the heart of a democratic society, its vitality depends upon its capacity to respond under pressure. For now, the heart continues to pump—but the stress test is just getting started.  

A lawyer and graduate of the Osgoode Professional , Shadi Nasseri's doctoral research addresses the profound legal and ethical concerns arising from neurotechnologies, including issues related to mental integrity, human dignity, personal identification, freedom of thought, accessibility, autonomy, and privacy.

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Artificial intelligence systems often “give the vibe” of complete automated processing without human involvement. However, as reminds us, upon a closer “vibe check” there are layers of unseen and under-appreciated human inputs, efforts, and labour involved. The efforts of those unseen human hands are, in fact, the engine of AI innovation.

Dr. Dagne is the Ontario Research Chair in Governing Artificial Intelligence and an Associate Professor at 91��ɫ’s new Markham campus in the School of Public Policy & Administration. He also teaches Property Law at Osgoode Hall Law School, where he is an Affiliated Researcher with IP Osgoode. His current project, which he recently presented at the at the University of Cape Town, highlights how copyright enables the proactive exploitation of digital workers’ contributions as inputs to AI training or, in some cases, AI-assisted outputs.

By bringing to the fore the roles of digital workers, Dagne hopes to unearth the collaborative creation that goes into the AI production chain and feeds into the AI output. His paper, “Unseen Hands, Invisible Rights: Unmasking Digital Workers in the Shadows of AI Innovation and Implications for the Future of Copyright Law”, is soon to be published in a forthcoming volume on IP’s Futures: Exploring the Global Landscape of Intellectual Property Law and Policy (Ottawa UP, 2025), which Dagne is co-editing with and . His chapter probes the future of copyright law, attempting to turn the focus of copyright to collaborative authorship. This move, Dagne argues, could respond to demands for the fair allocation of rights between digital workers, as authors or joint authors in some cases, and AI designers as exploiters of digital works. 

Digital Workers are the Lifeblood of AI Development

As , “[AI] doesn’t run on magic pixie dust… [AI training] is a job that actually takes quite a bit of creativity, insight, and judgment.” Such ingenuity involves the preparation of data works for the datasets used to train and build AI technologies, which consists of a number of decisions as to the kind of data to collect, curate, clean, label, abstract, index, etc. The process of dataset development starts with formulating the problem, which is the conceptualization of the machine learning task by making the problems “into questions that data science can answer”. The task conceptualization is typically the responsibility of the AI designer, which may be an AI company like Open AI or Anthropic AI, for example, or platform company like Microsoft, Meta, or Amazon. After the conceptualization process comes the data collection, refining, and measuring stage. Dagne’s focus is on the “digital workers” who enter the picture at this stage in the AI production process.

According to these digital workers contribute to the training process of AI systems in three steps: generating and annotating data (AI preparation), verifying model output (AI verification), and directly mimicking model behaviour to produce a service (AI impersonation).  They range “from higher-skilled, ‘macro-task’ […] workers [who] offer their services as graphic designers, computer programmers, statisticians, translators, and other professional services, to [those engaged in] ‘micro-task’ [work] which typically involve clerical tasks that can be completed quickly and require less specialized skills.” () As described by , “complex projects are broken down into smaller, easily accomplished tasks, which can then be distributed to a large number of workers.” Micro-task activities mainly involve the AI preparation aspect of AI training processes but can also include the AI verification and AI impersonation steps in AI training.

The Copyright Question

Much of the debate around copyright and AI has focused on whether using the underlying work of which inputs are constituted (the images, texts, musical works and other subject matter) for unauthorized learning constitutes copyright infringement. However, Dagne’s focus is on the copyright that can subsist over collected data, as we see in some and cases, and whether digital workers’ activities in the preparation of training data sets in the AI pipeline could itself give rise to a copyright interest. This question can be answered by examining the nature of digital workers’ contributions to the tasks assigned to them and the ownership of copyright under the contractual agreements that digital workers sign with platforms.

Digital workers in the AI production value chain collect raw data and help add extra meaning by associating each piece of data with relevant attributive tags. Although have argued that this attributive task is a mundane exercise that could ultimately be automated, others like have contended that tasks such as attribution will always be assigned to humans because of their capacity to recognize and classify data. Indeed, human intervention is now in demand to recognize the nuances and sophisticated details of specific data. As noted by , an example of such demand is in the medical field, where an understanding of scientific vocabulary is required.

From a doctrinal perspective, the copyright question is whether the contribution of digital workers described above meets the threshold of originality—which is defined, in Canadian law, by the Supreme Court of Canada’s ruling in , and requires more than trivial skill and judgment in the selection or arrangement of data. If so, we might ask whether recognizing the copyright status of such contributions could address these workers' invisibility. Even if, on account of originality, the tasks executed by digital workers amount to authorship, of course such authorship does not automatically translate into ownership. The ownership of the creative tasks conducted by digital workers as part of the collaborative venture is determined either by the workers’ status as employees or otherwise by contract—which means that it is determined in the context of significant power asymmetries and the routine exploitation of digital workers.

If copyright entrenches the inequities of an asymmetrical situation—by ensuring that the collective effort of digital workers in compiling essential datasets for AI training and AI development remains unseen and undervalued—Dagne thinks the time has come to confront its complicity. He suggests that, spurred by the arrival of AI, the copyright system needs to restructure the relationship between authors-as-(data)workers and corporate proprietors in pursuit of greater fairness.

‘Damola Adediji is a Visiting Researcher with IP Osgoode and Doctoral Candidate with the Centre for Law, Technology & Society at the University of Ottawa.

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Throughout her doctoral studies, Amanda Turnbull has grappled with the legal consequences of “machines doing things with words.” Her timely dissertation, Law, Language, and Authority: The Algorithmic Turn, completed in August 2024, offers a measured yet unflinching reflection on how artificial intelligence is transforming society and the law. Speaking over Zoom from her home in New Zealand, where she is now a at the University of Waikato’s Te Piringa Faculty of Law  Turnbull shared some insights from her research.

"With AI, there’s an algorithm at the end of the hammer.”

At the heart of Turnbull’s thesis is her contention that AI is “more than just a tool.” When we think of a tool, Turnbull suggests, we usually think of something like a hammer. There’s always a person at the end of the hammer, and they’re responsible for what the hammer does. In the context of algorithmic systems, commentators have proposed different alternatives for who that responsible party might be, including the programmer, the end user, and the company that owns the technology. But these approaches obscure the true novelty—and danger—of AI. With AI, Turnbull explained, “there’s an algorithm at the end of the hammer.”

Turnbull’s focus on algorithmically generated language reflects her thesis’s remarkable origins at the University of Ottawa’s Department of English. Although her original supervisor, the late Professor (Canada Research Chair in Ethics, Law & Technology), soon recognized that it belonged in a faculty of law, Turnbull’s dissertation maintains its indebtedness to mid-century philosopher of language, JL Austin—who, Turnbull was surprised to learn, was a close friend of the legal theorist . emphasized what words do in addition to what they mean. Adapting this framework to contemporary technology, Turnbull is less interested in what a generative AI like ChatGPT says than the difference it makes that a non-human actor says it.

The first of three “pillars” of Turnbull’s dissertation thus explores the consequences of AI’s participation in writing literary works. To be clear, “” according to Turnbull—but that doesn’t mean AI should have no legally cognizable role at all. Drawing on her early career as a classical flautist, Turnbull recognized that generative AI’s imitative reproduction of human-authored texts in its training data isn’t so different from the work of human artists. In her words, “there’s an amount of imitation that necessarily occurs when you’re being creative.”

Unexpectedly, Turnbull found inspiration in the “spectrum of authoring” developed by in the 13th century, long before the modern notion of authorship was developed. Generative AI, she asserts, resembles Bonaventure’s “commentator,” a mid-point between an author and a mere scribe, who clarifies and expands on pre-existing texts. By referring to generative AI as a commentator or “expositor,” lawmakers can reserve copyright for human authors without turning a blind eye to the authority embodied in algorithmically generated language.

That authority is at the centre of the second and third “pillars” of Turnbull’s research, which examine the legal implications of algorithmic contracting. As coined by , an algorithmic contract is a contract in which the main terms and conditions are drafted not by human actors, but by computer systems.

Key for Turnbull is how the systems behind algorithmic contracts exercise “derivative” authority without legal intent. For this reason, algorithmic contracting is “in no way” similar to earlier technologies such as click-wrap agreements, standard form contracts, or the archetypal pen and paper. In other words, there is no “functional equivalence” between algorithmic contracting and other platforms. Courts should therefore reconsider both the notion of technological neutrality and the application of intent-based contract doctrines, including the doctrine of unconscionability recently revived by the Supreme Court of Canada in .

In the third “pillar” of her thesis, Turnbull discusses how unconstrained algorithmic contracting creates the conditions for technology-facilitated sexual violence. She focused on Uber and the instances of sexual violence involving drivers and passengers documented in its 2019 safety report. Sadly, Turnbull described this chapter as “the easiest to write,” since it quickly “became obvious that this is a new way of exerting harm.” Yet the solutions to these problems are far from straightforward. In Uber’s case, the issue permeates the firm’s corporate culture and overall attitude toward innovation, she contends, which has failed to truly consider “the whole web of entanglements” impacting algorithmic language.

Ultimately, dealing fairly with AI will require “extraordinary ways of thinking” on the part of courts and regulators. But Turnbull is confident the law can adapt. The entire law of contracts and copyright, for example, can be seen as areas that have constantly adapted to new technologies. By approaching the algorithmic turn with both bravery and nuance, courts can learn to recognize AI as something that’s more than a tool, but no substitute for genuine human authority and intent.

Going forward, Turnbull is keen to use her dissertation, which was supervised by IP Osgoode Director , as a basis for further explorations of technology-facilitated gender-based violence such as platform violence and “onlife” harm—a term to describe the intersection between experiences online and in ‘real life.’ At the same time, Turnbull is interested in how algorithms have played a positive role in certain legal contexts. Although, as she says, “we’re hot to jump on technology and focus on the negatives,” in a forthcoming article on the 1999 Canada-US Pacific Salmon Agreement, she and co-author Donald McRae will explore how, “in this case, the algorithm solved the dispute.” Turnbull also plans to publish her dissertation as a book and to return to another book she began writing even before beginning her PhD—a fictional novel that is, aptly, about Austin, Hart, and the father of computer science, Alan Turing…

John Nyman is a student at Osgoode Hall Law School (JD '26) and an IP Osgoode JD Research Fellow

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In March 2022, the Canadian Intellectual Property Office (“CIPO”)  its first artificial intelligence (AI)-authored copyright registration of a painting co-created by the AI tool, RAGHAV Painting App (“RAGHAV”), and the IP lawyer who created RAGHAV, Ankit Sahni. RAGHAV is the first non-human “author” of a copyrighted work. However, Canadian courts  that “[c]learly a human author is required to create an original work for copyright purposes” (para 88). Though the AI tool is a co-author with a human, the registration suggests that both RAGHAV and Ankit Sahni can constitute an author under the copyright regime and  amongst Canadian artists. Though the landscape in Canada is still unclear, the US Copyright Office (“Office”)  a clarification on March 16, 2023, about its practices for examining and registering works that contain material generated by artificial intelligence (AI) technology.

The Human Authorship Requirement

The Office  that the term “author,” used in both the US Constitution and the Copyright Act, excludes non-humans. To qualify as a work of ‘authorship,’ a work must be created by a human being and works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author are not registrable. This threshold reflects the Canadian copyright regime,. The author  significant original expression to the work that is  to be characterized as a purely mechanical exercise.

The US Copyright Office’s Approach to AI-Generated Work

The Office provided important  on assessing the protectable elements of AI-generated works. It begins by distinguishing whether the ‘work’ is one of human authorship, with the AI tool merely being an assisting instrument, or whether the protectable elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were conceived and executed not by man but by a machine.

If the machine produced the expressive elements of the work, it is not copyrightable. This guidance is critical for  surrounding Chat-GPT, where the AI tool receives a prompt from the user, and the user does not exercise ultimate creative control of the output. The Office provided an  where a user instructs an AI tool to “write a poem about copyright law in the style of William Shakespeare”. Given that the user contributes little to no expressive elements to the AI-generated output, the output is not a product of human authorship or protected under the US Copyright Act.

However, the Office also  that, in some cases, AI-generated works might contain sufficient human-authored elements to warrant copyright protection. This may apply in cases where the human selects or arranges the AI-generated elements or modifies the AI-generated material to a degree where it constitutes original expression. The analysis seeks to determine whether a human had ultimate creative control over the expression and formed the traditional elements of authorship.

This guidance is in response to a recent review by the Office of a  titled “Zarya of the Dawn” containing human-authored elements combined with AI-generated images. While the Office  that the author, Kristina Kashtanova, owned the work’s text and the selection, coordination, and arrangement of the work’s written and visual elements, copyright protection did not extend to the images generated by the AI tool, Midjourney. Though Kashtanova edited the Midjourney images, the Office held that the creativity supplied did not constitute authorship.

How will this apply in Canada?

Given the registration of RAGHAV as an author under Canadian copyright law last year, it remains to be seen whether CIPO will follow a similar assessment as the US Office and revisit the decision to register an AI-generated work as a work of joint authorship. However,  question whether moral rights, which are not part of the US regime, will extend to AI authors and if AI authorship will alter the copyright term of the last living author’s death plus 70 years. The increasing traction of AI warrants similar guidance from CIPO regarding the status of AI authorship under Canadian copyright law.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School.

The Institute of Electrical and Electronics Engineers (IEEE), a professional organization for engineers and technology experts, recently announced the launch of the IEEE GET Program, aimed at providing free access to AI ethics and governance standards. The program is part of IEEE's ongoing efforts to promote responsible AI practices and help organizations develop and implement ethical AI systems.

The opened seven standards for public access:

1. Age-Appropriate Digital Services
2. Addressing Ethical Concerns during System Design
3. Transparency of Autonomous Systems
4. Data Privacy
5. Transparent Employer Data Governance
6. Ethically Driven Robotics and Automation Systems
7. Assessing the Impact of Autonomous and Intelligent Systems (“A/IS”) on Human Well-Being

Assessing the Impact of A/IS

The most cited of the standards is the , which addressed the growing concern of how autonomous and intelligent systems may affect society. This standard provides a structured approach to evaluating the impact of A/IS on individuals, communities, and society, and helps organizations ensure that their systems are developed and deployed in a manner that supports human well-being. Recommended practices aim to bring an increased awareness about well-being concepts and indicators for A/IS and an increased capacity to monitor, evaluate, and address the well-being impacts of A/IS. Successful application of the standard includes implementing the ability to evaluate the ongoing well-being impact of A/IS on users and stakeholders while continuing to improve the system to safeguard human well-being, resulting in a greater ability to avoid unintentional harm.

The Standard also suggested numerous domains of well-being and accompanying indicators that system designers should be concerned with. These domains pertain to individual well-being —satisfaction with life, affect/feelings, and psychological well-being, social well-being —community, culture, education, economy, health, and work, and regulatory domains —environment, government, and human settlements. The Standard noted that these suggestions are a starting point for selecting indicators and that “indicators should be adapted to fit the circumstances of measuring and gathering data about the well-being impacts for an A/IS on user(s).”

Ethics and Systems Design

The is also frequently cited. This Standard provides a set of guidelines and best practices for organizations that engage in system and software engineering to make value-based ethical system design and investment decisions.

A number of interesting points to consider are included within the Standard Model Process. The standard provides guidance to organizations on establishing key roles in Ethical Value Engineering Project teams. These teams are then tasked with defining how a system is expected to operate from the users’ perspective Concept of Operations — identifying stakeholders and determining the context of use and potential for ethical benefit or harm (Context Exploration). There is also an Ethical Values Elicitation and Prioritization Process, which aims to obtain and rank values and value demonstrators, followed by an Ethical Requirements Definition Process that guides the defining of value-based system requirements to reflect the prioritized core values and their value demonstrators. Finally, the Standard also sets out an Ethical Risk-Based Design Process and a Transparency Management Process, guiding the realization of ethical values and required functionality in designing a system and how to inform stakeholders of the system’s implementation of ethics.

Impact on AI

that these IEEE standards are already being incorporated into AI governance. For instance, the European Union's Artificial Intelligence Act (“EU AI Act”) references many of the components that the IEEE makes available in this package. This will likely continue to be relevant both for regulators and AI developers —“TÜV SÜD sees a strategic advantage for those looking to demonstrate eventual compliance to human-centric regulatory measures or market pressures to leverage these IEEE standards and certifications.” Developing ethical AI systems is a multifaceted problem which requires extensive deliberation by organizations involved with AI systems development. The release of free standards by an authoritative governing body will likely immensely benefit everyone involved.

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Mac Mok is a 3L JD candidate at Osgoode Hall Law School. This article was written as a requirement for Prof. Pina D’Agostino’s IP Intensive Program.

Artificial intelligence (AI) systems have entered every facet of our daily lives. The availability of massive data sets has been leveraged by AI systems to . The healthcare field has been no exception, with the Humber River Hospital now housing “”, an AI system that can track the flow of patients from their intake to discharge, and help healthcare providers make more informed decisions to improve overall efficiency and deliver better care.

, however, can produce discriminatory consequences. Examples include the Amazon recruiting algorithm that penalizes resumes containing the word “women” and the COMPAS algorithm used in criminal sentencing that was more likely to penalize African-American defendants. During AI system development, biases can appear in the training data when historical human biases contribute to the generation of such data, or when the data is imbalanced, that is, some groups are overrepresented and others are underrepresented.

can also impact healthcare AI systems. Recent regulatory efforts have begun addressing this issue. Health Canada, in a joint effort with the US Food and Drug Administration and the U.K. 's Medicines and Healthcare Products Regulatory Agency, has identified to inform the development of Good Machine Learning Practices (the dominant method used to train AI systems). In particular, the third guiding principle requiring that clinical study participants and data sets are representative of the intended patient population raises the importance of managing any bias. Another regulatory effort is the Artificial Intelligence and Data Act (AIDA) that was tabled as part of Bill C-27 to regulate the use of AI systems in Canada . of the AIDA puts the onus on the person(s) responsible for the AI system to “establish measures to identify, access and mitigate the risks of harm or biased output that could result from the use of the system”. Healthcare AI systems could also fall under such regulation.

The guidelines and regulations above put much responsibility to mitigate bias on the shoulders of AI developers. Logically, would be in the best position to recognize and address biases in the system as they have direct access to the training data and may correct important deficiencies. Arguably, however, such as the doctors seeking AI to solve a problem, and the end users of the system such as patients should also provide critical feedback to AI developers. Particularly in the healthcare field, where understanding training data and interpreting system outputs may require years of medical training, medical practitioners may play a key part in spotting biased inputs and outputs, allowing for correction of AI system deficiencies. Thus, the guidelines and regulations on preventing biased AI systems should consider what role doctors and patients have in developing responsible healthcare AI tools.

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