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.

The rapid proliferation of artificial intelligence (AI) technologies across corporate value chains has exposed fundamental gaps in traditional approaches to ensuring respect for human rights. This article analyzes the transition from fragmented compliance mechanisms to unified governance models that seek to embed human rights standards within AI deployment, focusing in particular on the European Union’s regulatory framework and its global implications. A central focus is placed on the EU’s AI Act, which introduces the Fundamental Rights Impact Assessment (FRIA) as the first mandatory evaluation mechanism for selected high-risk AI systems. We demonstrate that FRIA marks a significant regulatory innovation. Alongside FRIA, we examine the Council of Europe’s voluntary HUDERIA methodology, which while conceptually aligned with FRIA, enables organizations to undertake comprehensive rights assessment across their entire AI portfolio. We argue that the convergence of mandatory (FRIA) and voluntary (HUDERIA) frameworks strengthens corporate responsibility for human rights in AI, extending ethical obligations where legal norms may not reach. However, we also highlight the real-world challenges to universalizing these standards – growing regulatory divergence between the EU’s rights-driven approach and the US innovation-focused “America’s AI Action Plan.” The resulting tension threatens the global harmonization of human rights protections in AI.

The boundary problem (BP) asks who ought to be included by democratic procedure. Initially articulated as a challenge for democratic citizenship, the BP is later reframed as a difficulty faced by the ‘democratiing AI' (DAI) agenda. By mapping different senses of DAI, this article aims to provide a resolution to the BP in the context of DAI. In particular, it notes that DAI shifts our attention away from a crucial factor to make ethical decisions around AI: the power disparities within organizations that put employees advancing ethical AI in a vulnerable position. In this context, I show that workplace democracy provides a more promising normative aspiration than the DAI agenda, although some elements of the latter, such as its emphasis on subjecting tech firms to greater democratic control, serve to mitigate certain problems that workplace democracy is unable to address, for example, when members of tech firms unanimously prioritise ethically concerning AI practices.

This article examines how artificial intelligence (AI) systems displace the foundational structures that uphold legal legitimacy. Traditionally anchored in legal certainty, accountability and enforceability, which this study conceptualizes as the “Tripod of Legal Legitimacy,” law’s normative authority is increasingly undermined by opaque, adaptive and privately governed algorithmic infrastructures. AI systems embed regulatory functions such as adjudication, classification and enforcement directly into technical design, often operating beyond the reach of public oversight or judicial review. Through comparative analysis of public frameworks like the EU’s Digital Services Act and AI Act, alongside private governance regimes such as Meta’s Oversight Board and OpenAI’s safety protocols, the article demonstrates how law is displaced both functionally and structurally. A process-based model contrasts traditional legal governance cycles with AI-induced governance cycles, revealing a recursive erosion of legal authority. The paper advances a theoretical framework called legal displacement to diagnose this shift and proposes policy strategies for reconstructing legal legitimacy through traceability, binding enforcement, and jurisdictional coordination. Ultimately, the study argues that reclaiming legal authority in the age of algorithmic governance requires institutional transformation grounded in procedural transparency and democratic accountability, rather than relying solely on ethical frameworks or voluntary compliance regimes.

Artificial intelligence (AI) presents unique regulatory challenges due to its rapid evolution and broad societal impact. Traditional ex ante regulatory approaches struggle to keep pace with AI development, exacerbating the “pacing problem” and the Collingridge dilemma. In response, experimentalist governance–particularly through regulatory sandboxes (RSs)–has emerged as a potential solution. This paper examines AI RSs within the European Union’s Artificial Intelligence Act (AI Act) from a law and economics perspective, investigating their capacity to address market and government failures and enhance regulatory efficiency compared to traditional command-and-control mechanisms. Applying an economic analysis of law framework, the paper evaluates how RSs can mitigate information asymmetries, reduce negative externalities, and facilitate iterative regulatory learning while promoting responsible AI innovation. It further analyses how RSs may correct specific government failures, including regulatory capture, rent-seeking, and knowledge gaps. Drawing comparative insights from FinTech, the paper identifies the institutional design features necessary to ensure their effectiveness and resilience. While RSs offer a flexible and innovation-friendly governance model, their success ultimately depends on sound institutional safeguards, proportionality, and alignment with broader policy objectives. The paper contributes to ongoing debates on experimentalism in AI governance by proposing design principles for effective, accountable, and adaptive sandboxes.