The rapid rise of artificial intelligence (AI), especially generative AI, is transforming industries and redefining corporate governance. AI now influences key business functions such as finance, compliance and risk management, presenting boards of directors with new oversight challenges. While AI offers strategic opportunities, it also introduces complex risks – algorithmic bias, privacy breaches, cybersecurity threats and ethical concerns. Despite its growing relevance, AI governance remains underdeveloped in Canada. From an empirical legal standpoint, the role of boards in managing AI-related risks is still emerging and largely unexplored. This gap is increasingly critical amid rising expectations from regulators, investors and the spread of soft law instruments like voluntary codes and proxy advisor guidelines. This article examines how Canadian TSX 60 companies are responding to AI governance challenges. It situates the issue within Canada’s corporate governance framework and presents an empirical study revealing a disconnect between recognizing AI as a strategic risk and formally integrating it into governance structures. Drawing on academic research and international best practices, the article assesses the responsiveness of Canadian governance standards and offers recommendations for regulators seeking to modernize oversight in an era of technological disruption and ethical complexity.

This paper examines how China operationalizes the concept of trustworthy artificial intelligence (AI) in its judicial system through the integration of socialist core values (社会主义核心价值观). While it would be difficult for liberal democracies to translate ethical principles into technical and legal mechanisms for AI governance, China has achieved a high degree of technical maturity and normative clarity in this field. This development has resulted from the authoritarian fusion of legality and morality under the leadership of the Communist Party of China and its translation into the process of AI value alignment. Drawing on legal texts, technical standards, and institutional practices, the paper argues that China’s approach to judicial AI value alignment transforms “ethical AI” from a safeguard of justice into an instrument of political control. In contrast to rights-oriented frameworks that would need to preserve ethical pluralism and procedural safeguards of liberal legal systems, China’s model mandates adherence to a single, state-defined morality. The analysis situates China’s judicial AI within broader debates on authoritarian legality and value alignment, demonstrating how the pursuit of “trustworthiness” can serve illiberal ends. The paper concludes by reflecting on the implications of China’s approach for global AI governance and the future of algorithmic justice.

Public participation in artificial intelligence (AI) development by public administrations is increasingly proposed as a potential silver bullet for mitigating the risks associated with the use of AI in the public sector. This paper examines whether public participation can serve as a viable solution from a doctrinal and practical perspective by analyzing the availability of legal avenues for natural and legal persons to participate in AI development procedures by the EU and member states’ public entities. While the idea is appealing, there are significant obstacles. Notably, EU law currently lacks specific legal frameworks to enable public involvement in the design and development of AI systems by public entities. Neither traditional European administrative law nor the new Artificial Intelligence Act provides effective legal avenues for involving the broader public in AI development in the public sector, posing challenges to implementing meaningful participation. Furthermore, drawing an analogy from environmental law, where public participation often ends up being formalistic and ineffective, suggests that similar outcomes might arise in the context of Public-Sector AI development. Therefore, despite the theoretical advantages of involving the public, this paper argues that, without robust legal structures and meaningful procedural designs, public participation in Public-Sector AI development may ultimately fall short of achieving its promises, proving to be a dead end.

Artificial intelligence (AI) systems already govern – they rank speech, allocate attention, filter applicants and triage claims. Technical improvements in safety and alignment do not determine whether publics will accept that governance. Legitimacy, on the sociological view used here, turns on recognition: whether power appears rule-bound, justified and situated in institutions that speak for those it governs. Social media demonstrates the problem: distribution improved even as publics questioned who sets the rules, producing recurrent conflicts grounded in missing authorization. As AI embeds upstream of more domains, the risk is repeating that crisis at larger scale – systems that perform yet lack legitimacy, inviting resistance and regulatory backlash. This Article makes legitimacy an explicit regulatory aim for AI. It identifies three persistent deficits: opacity blocks audience-understandable reasons; private actors wield public-facing authority without recognized authorization; and administrative automation strains participation, reason-giving and review. Legal tools can structure recognition. Thin legality supplies visible forms – publicity, stability, consistency – that signal non-arbitrariness. Thick legality adds public authorship, audience-facing reasons and contestation. This Article distills this into three principles: Integration seats AI rule-setting in recognized venues; Familiarity presents rules in locally credible forms; and Contestation guarantees a credible second look. Together, these convert performance into justification and justification into authority publics recognize as rightful.

As artificial intelligence (AI)-assisted judicial decision-making (AIJDM) becomes more common globally, understanding what affects court users’ trust in processes where this technology is used becomes crucial for maintaining judicial legitimacy. This study investigates how different types of explanations about AI for assisting judicial decision-making affect trust through procedural justice mechanisms. Using a 3 × 2 between-subjects experiment (n = 859), we measured trust in judicial decision-making processes where AI was used across six conditions, varying the explanation type about the AI – process explanations (how the AI system works), outcome explanations (how it reached a specific recommendation on a decision) or no explanation – and the decision context (liability vs. quantum). Mediation analysis showed that providing explanations (regardless of type) substantially increases both cognitive and affective trust compared to no explanation, in both liability and quantum decision-making contexts. Critically, procedural justice perceptions strongly mediated this effect, with respect emerging as the dominant mediator (accounting for 61–72% of indirect effects), followed by neutrality, while voice contributed negligibly. This mediation was particularly strong for affective trust (89.6% of total effect) compared to cognitive trust (69.8%). These findings extend procedural justice theory to AI-assisted judicial contexts and suggest that communicating explanations about AIJDM is a vital mechanism for signaling respect to participants in a court process and maintaining judicial legitimacy in increasingly AI-assisted judicial processes.

The use of AI in courts is highly contested, with positions ranging from outright opposition to unmitigated enthusiasm. However, normative assessments of its deployment often rely on standards or analogies that are unclear in their pedigree, coherence and scope conditions. This article explores how theories of legitimate authority can offer some guidance. Legitimacy theories serve to assess institutions and how they use their authority. The framework allows us to identify central issues and dilemmas, order different normative standards and identify relevant differences and similarities across time and jurisdictions. To undertake such a legitimacy analysis of AI in courts, we identify six forms of delegation to AI in courts (servant, interlocutor, advisor, partner, agent and trustee) and analyse the legitimacy implications for six aspects of adjudication (legal process, inventorising, problem-solving, reasoned judgment, legitimation and discretion). In some areas, AI may enhance both the efficiency and quality of the judicial process, while we argue that the delegation of judicial power to AI in aspects which require extensive human discretion and human presence is difficult to justify.

While artificial intelligence (AI) offers promise as a tool for efficiency and access to justice, in public administration it also threatens to reproduce the perversity in Franz Kafka’s parable “Before the Law”: systems that mimic legality while rendering the law opaque and unreachable. I develop the concept of “Digital Kafkaism” to describe this pathology, and argue that a crucial surface of contest lies in the design of human–computer interactions that mediate administrative decisions. Drawing on administrative law’s core values (such as reason-giving, accountability and contestability), I argue that these can be preserved within socio-technical systems if we treat Responsible AI (RAI) not as a matter of stating abstract principles or technical fixes, but as a design brief. Lawyers, I argue, have a distinctive contribution to make in this interdisciplinary project: to ensure that the gateway to the law remains genuinely open by embedding public law commitments in the architectures of digital administration. To this end, I propose a programmatic set of design-facing requirements to translate public-law values into operational constraints for AI-mediated administration.

Existing artificial intelligence (AI) liability scholarship focuses largely on narrow, task-specific systems. This article asks how existing liability frameworks would function – or fail – when confronted with artificial general intelligence (AGI). It examines four liability regimes – strict product liability, fault-based negligence, vicarious liability and strict liability for hazardous activities – across five jurisdictions (the European Union, United States, United Kingdom, Singapore and China), concentrating its comparative analysis on product liability, the regime most affected by digital-era reform, while situating the other three within the broader structural critique that follows. These regimes embed assumptions about predictability, human control, causal traceability and task-specificity that AGI would structurally violate. AGI challenges liability law not merely evidentially but structurally, destabilizing the core legal concepts of defectiveness, foreseeability, proximate causation and attribution of agency on which existing regimes depend. This discussion reflects the current regulatory context: the withdrawal of the EU AI Liability Directive proposal and the adoption of the revised Product Liability Directive (Directive [EU] 2024/2853). Drawing on nuclear and environmental liability analogies, the article proposes an anticipatory liability architecture: channeled strict liability with mandatory capitalization, capability-calibrated duties of care and an international compensation fund.

This article explores attempts to regulate the use of “generative” artificial intelligence (GenAI) by the judicial arm of government in jurisdictions in Australasia. There are significant concerns about the use of GenAI-produced materials in courts, including wasting the courts’ resources, fairness concerns and undermining public trust in legal processes. Courts such as those in Singapore, New Zealand and the Australian states of Victoria, Queensland, New South Wales, South Australia and Western Australia have issued guidance or practice notes seeking to direct or limit the use of GenAI tools. Other courts have not issued guidance, but drawn on a range of regulatory strategies to respond to the use of GenAI in proceedings. In this article, we focus on four Australasian jurisdictions: Australia, Hong Kong, New Zealand and Singapore. Responsive regulation invokes the concept of a pyramid of regulatory mechanisms, moving from lower-level options aimed at education and motivating compliance through to punitive measures designed to deter and even to punish. The variety of regulatory strategies courts have adopted includes prohibition, disclosure and awareness-raising. Like the genie, GenAI will not now go back into the bottle as both lawyers and self-represented parties continue to make use of it. We therefore address the future feasibility of current regulatory strategies in this context, and the importance of continuing to refine the regulatory approach.

Artificial intelligence (AI) is increasingly used in public administration, yet its opacity raises concerns about arbitrary exercises of state power. This article argues that alongside regulatory frameworks, the Rule of Law supplies an independent basis for limiting state deployment of AI. Drawing on widely accepted ideas associated with the Rule of Law – predictability (decisions must guide conduct ex ante) and procedural clarity (rationales must be knowable and contestable ex post) – the article shows how these standards constrain government use of AI. It examines why black box systems frustrate legal guidance and contestability, and why explanations are often the minimally sufficient remedy. A welfare-benefits hypothetical illustrates the stakes of opacity versus explainability. The article concludes that Rule of Law limits offer a distinct and operationalizable basis for determining when state deployment of AI contravenes fundamental commitments, demonstrating that not all government uses of AI should be permitted.

This article investigates how to translate abstract responsible artificial intelligence (AI) principles into practical constraints for public-sector AI systems. Focusing on due process, transparency, fairness, reason-giving and proportionality, the paper uses administrative and constitutional law doctrines to scrutinize AI deployments in government. The purpose of this comparative analysis is to evaluate how the divergent frameworks of the United States (US), the European Union (EU) and Singapore fulfill responsible AI principles. The paper contrasts the EU’s unified hard-law model with the fragmented preemption approach of the US and the facilitative soft-law guidelines of Singapore. Furthermore, the analysis explores how legal norms can be integrated throughout the AI lifecycle, addressing the inherent tensions between technical performance and legal compliance. Illustrative case studies – including the Dutch childcare-benefits algorithm, the UK exam-grading fiasco, the US Internal Revenue Service audit controversies and Estonia’s successful Kratt AI implementation – highlight the severe consequences of algorithmic opacity and the benefits of proactive governance. Drawing on these insights, the paper argues that multidisciplinary governance is essential. The article’s original scholarly contribution lies in systematically mapping administrative and constitutional law doctrines onto AI requirements across the AI lifecycle, proposing a concrete techno-legal architecture that ensures automated public decisions comply with enduring legal norms.

The paper examines the interaction between the EU’s Artificial Intelligence Act (AIA) and the 2024 Product Liability Directive (PLD). Building on the premise that safety regulation and liability law constitute “two sides of the same coin,” it analyses the principal points of connection through which the AIA may shape liability under the PLD. These include defectiveness, PLD defences and liability for substantial modifications. With regard to the AIA, the analysis focuses on the mechanisms that are characteristic of EU product safety law, such as harmonised standards and conformity assessments. The paper demonstrates that the PLD contains several mechanisms capable of translating the AIA’s safety obligations into liability benchmarks, enabling both instruments to reinforce each other’s effectiveness. PLD claims may serve as a vehicle for private enforcement of the AIA, while the AIA can facilitate the assessment of liability. At the same time, the PLD’s traditional limitations – the product–service divide, restricted categories of compensable damage and exclusion of the user side of the value chain – constrain alignment precisely in areas where AI-related harms typically arise. These gaps are likely to be filled by diverging national liability regimes, increasing legal uncertainty and limiting the PLD’s capacity to provide a coherent EU-wide liability framework for AI-related harm.

The widespread use of generative artificial intelligence (GenAI) has increased the risk of reputational harm caused by false or “hallucinated” outputs. This article explores how the European Union legal framework, together with selected Member States’ legal systems, addresses liability and remedies for such harms, with particular attention to defamation, product liability, noncontractual liability and data protection. Then, the article analyzes the remedies available to injured parties, focusing on claims for damages, injunctive relief and the exercise of data protection rights. While national legal systems generally allow compensation for nonpecuniary damage resulting from violations of personality rights, the amounts awarded are likely to be minimal due to the limited dissemination of AI-generated content. Consequently, the article argues that prohibitory injunctions aimed at preventing the publication of identical or equivalent false information frequently offer a more robust form of protection than financial redress. Finally, operators of GenAI systems must comply with the principles relating to the processing of personal data, particularly the principle of accuracy and ensure the effective exercise of data protection rights; otherwise, data subjects may bring an action for compensation against the data controller under Article 82 of the General Data Protection Regulation.

The rapid proliferation of generative artificial intelligence (GenAI) technologies has intensified debates in copyright law regarding the scope of exclusive rights, the role of exceptions and limitations and the allocation of liability among the multiple actors operating within complex technological value chains. This article maps and critically examines the principal copyright liability-related issues currently emerging under EU law as a result of the interaction between the Directive on Copyright in the Digital Single Market, the Artificial Intelligence Act and the broader EU copyright acquis. The analysis is structured around three core phases of the GenAI value chain: (i) the training phase, (ii) the storage and distribution of trained models and (iii) the generation and dissemination of outputs. In doing so, it analyses the relevance and limits of the text and data mining exceptions, the legal significance of model “memorization” and the qualification of potentially infringing outputs. Beyond this structural analysis, the article identifies a set of contentious issues that are increasingly surfacing in litigation and regulatory debates. These include the legal implications of retrieval-augmented generation, the potential infringement of moral rights, the circumvention of technological protection measures and the evolving framework governing remedies for copyright infringement.

Data privacy and protection are inextricably intertwined, whereas the former often slides into a national security concern. Amid geopolitical tensions, the U.S. aims to preserve TikTok’s independence and prevent its use in cognitive warfare. The landmark case presents potentially groundbreaking trade-offs for the Committee on Foreign Investment in the United States and the balance between data privacy and national security. The TikTok ban can be seen as a Catch-22 situation, presenting a Hobson’s choice for both the U.S. and China in the ongoing data war. The U.S. faces national security concerns regarding Chinese influence over the app’s data and its potential use for surveillance and propaganda. Conversely, banning TikTok risks setting a precedent for censorship and digital platform manipulation, undermining the U.S.’s long-standing commitment to free speech and open markets. The dilemma lies in whether to prioritize national security over the principles of free expression and open trade or to accept the risks posed by adversary-owned platforms operating in the U.S. This decision underscores rising tensions as the two powers grapple with data governance, digital sovereignty and geopolitical rivalry – an outcome that will shape global data governance and the technological landscape for years to come. A paradox emerges as China’s ideological aims and the U.S.’s technical concerns converge on a cutting-edge global challenge, that is, cognitive warfare in shaping global discourse and public opinion. This study examines how these conflicting forces shape TikTok’s quest to become a global platform, delving into the root causes of these tensions and exploring strategies to address the associated challenges.

In an increasingly data- and AI-driven economy, integrating ethical values into algorithmic decision-making is becoming ever more pressing. Benevolence is a fundamental value in human and organizational interactions, fostering trust, fairness and long-term relationships. Companies, particularly in the financial sector, must balance economic efficiency with social responsibility. As business decisions become increasingly automated, the question arises whether and how benevolence can be programmed. Research on artificial benevolence explores how AI systems can maximize not only efficiency but also trust, mitigate discrimination and align with broader corporate purposes. This requires an interdisciplinary perspective integrating technical, ethical and legal dimensions. While existing regulations increasingly shape AI-driven decisions, the question remains how artificial benevolence can be implemented and legally anchored.

A brief examination reveals the multifaceted legal challenges involved in implementing artificial benevolence in consumer banking. In addition to the omnipresent questions of data privacy and liability, risks of discrimination shape the legal debate on a practice of benevolent AI-driven decision-making. Further issues stem from governmental participation in consumer banks based on the model of a public savings bank. The social impact of decisions made by banks in their interactions with private customers on tenancies, real estate projects and the provision of daily necessities is exceedingly important to fundamental rights. Another relevant question is whether administrative bodies could commit themselves to derivative participation claims. This article outlines these issues in the context of the growing regulatory framework provided by European Union law.

As boards engage with the adoption of artificial intelligence (AI), including agentic AI to drive operational efficiencies, this presents new opportunities for profit maximization. AI adoption is increasingly identified with employee role displacement in companies, and the interests of employees as stakeholders require exploration. A novel question posed is whether, in an age of AI ascendancy, AI may warrant being given stakeholder status as its role in the company approximates or eclipses that of human employees. The article probes four distinct models of corporate purpose within the directors’ duty to act in the best interests of the company – the shareholder primacy model, the Enlightened Shareholder Value model, the stakeholder friendly model and the stakeholder value model, highlighting the available scope for directors to accommodate the interests of employees around AI adoption in decision-making by boards around AI. It is concluded that, given the degree to which directors are insulated from legal scrutiny in relation to their best interests duty, adopting a wider law in context approach to promote employee welfare would serve the interests of employees, directors and companies alike. This would see directors engaging meaningfully with employees and providing opportunities for reskilling to adapt to the age of AI.

The governance of Artificial Intelligence (AI) fundamentally depends on the implementation of risk management standards. Article 9 of the EU AI Act exemplifies this challenge, as it relies on indeterminate terms, such as “reasonably foreseeable risks” and “acceptability of risk”, to define the scope of the provision. This paper argues that such open risk management provisions require an economic framework in order to establish a coherent and innovation-friendly standard of care.

Drawing on the theoretical parallels between risk management and tort law, the analysis demonstrates the importance of cost-benefit and risk-utility models in transforming ambiguous legal standards into actionable ones. However, a purely quantitative approach proves insufficient for several reasons, including the risk of “metrics shopping” and the protection of fundamental rights. Consequently, the paper proposes a hybrid approach to risk management that integrates quantitative metrics with qualitative safeguards. Furthermore, in addressing the challenge of hindsight and outcome biases in ex-post enforcement, the analysis recommends applying the Business Judgment Rule (BJR) logic from corporate law. By limiting the standard of review to the quality of ex ante decision-making, this framework can be applied more widely to the management of AI risks.

Corporations play a foundational role in the global economy, yet persistent gaps remain between corporate governance ideals, legal frameworks and organizational practice. The rapid integration of artificial intelligence (AI), particularly generative AI and large language models, intensifies these challenges by reshaping corporate decision-making, compliance and accountability. This paper examines how AI both promises to alleviate long-standing governance problems, such as information asymmetries and managerial opportunism, and generates new risks arising from opacity, automation bias and diminished human oversight. It analyzes five interrelated areas of tension: the emerging AI governance gap, models of AI integration within corporate structures, the adaptation of directors’ duties to algorithmic decision-making, the transparency paradox created by AI-mediated disclosure and the problem of anthropomorphism, whereby attributing agency to AI risks obscuring human responsibility. Rather than offering definitive solutions, the paper identifies critical questions that corporate law must confront as automated and semi-automated corporations become an established reality. It argues that sustaining legitimacy in the AI era requires renewed emphasis on human judgment, board-level oversight and adaptive governance frameworks capable of reconciling technological power with legal accountability and societal expectations.