It is widely accepted that tort law operates according to a hierarchy of protected interests. Some commentators suggest that this hierarchy can be put to dispositive uses in cases characterised by a clash of interests held respectively by the claimant and defendant (the inferior interest giving way). Others argue that thinking in terms of a hierarchy of interests sheds light on three unusual aspects of tort law: viz. the existence of torts that are actionable per se, the existence of strict liability torts, and the existence of actions in which injunctive relief is routinely awarded even though compensatory damages are tort law's default remedy. This article tests both claims. It concludes that an intuitively appealing hierarchy of interests can be identified, and that it might well possess dispositive significance all other things being equal. But it also observes that all other things are seldom equal, and that departures from the hierarchy occur for various reasons that can be clearly identified and which should be borne in mind when thinking about its dispositive utility. It also urges caution in making connections between the status of certain interests and the fact that they are protected by torts that are actionable per se, strict liability torts and torts in connection with which injunctions are awarded almost as a matter of course.

Insurers are increasingly using big data analytics and artificial intelligence in rating risks and customising insurance products particularly in the context of consumer insurance. The primary aim of this article is to elaborate the extent to which the legal rules in force could ensure that consumers are not treated unfairly as a result of the use of such disruptive technologies. Relevant insurance law principles and doctrines are also considered as part of this analysis. The article concludes that despite the protection provided to consumers by data and consumer protection legislation, unregulated and unlimited use of data analytics and algorithms in the risk assessment process could create significant difficulties for consumers. It is argued that further regulation, especially making regular audits essential for insurers employing such technologies in risk assessment process, is required. The article also finds that the use of artificial intelligence in customising insurance products does not present similar degree of difficulties for consumers.

This article examines global vaccine inequity during the COVID-19 pandemic. We critique intellectual property (IP) law under the 1994 WTO TRIPS Agreement, and specifically, the role that IP has played in enabling the inequities of production, distribution and pricing in the COVID-19 vaccine context. Given the failure of international response mechanisms, including COVAX and C-TAP, to address vaccine inequity, we argue the TRIPS waiver proposal should be viewed as offering a necessary and proportionate legal measure for clearing IP barriers that cannot be achieved by existing TRIPS flexibilities. Finally, we reflect on the waiver debate in the wider context of TRIPS and the need to boost global pandemic preparedness.

Leading cases show Quistclose trusts being used by companies nearing insolvency. Their use in this context raises serious normative problems: it may prefer the beneficiary to the company's other creditors, and creates a misleading impression that trust funds are in fact free of trust. Building on the emergent normative literature on Quistclose trusts, we first examine which Quistclose trusts are currently allowed under company law and the law of corporate insolvency. We then discuss the normative question as to which Quistclose trusts should be allowed, given the principles of these branches of the law.

The literature on the identification of rules of customary international law is extensive. Commentators have focused on isolating the methodologies by which international courts and tribunals identify customary international law, with most of the debate revolving around the use of induction, or deduction and assertion as methods of custom identification. However, the existing literature has overlooked that the choice among custom identification methodologies takes place behind closed doors, during confidential deliberation processes. When all that scholars see may be deduction or assertion, international courts and tribunals may have ascertained the existence of customary rules by induction, but induction may not have made it into the final text of the decision. This article elaborates on the impact of judicial deliberations at the International Court of Justice on the choice among custom identification methodologies. It argues that individual-driven stages of deliberations favour custom identification by induction, while collegial stages promote custom identification by non-inductive methodologies.

Professor Goode once observed that “Novation need not be left to ad hoc agreement; it is open to the parties to provide for it in advance and in particular to establish a contractual mechanism by which novation takes place automatically on the occurrence of a designated act or event”. This deceptively straightforward proposition is examined in the present article. It explores the legal footing for, and the risks in adopting a pristine version of, the proposition, and considers possible safeguards that may be incorporated within the process of scrutiny, if in any case there arises concern over the effectiveness of a novation that is undertaken pursuant to consent given in advance.

Clear understanding of artificial intelligence (AI) usage risks and how they are being addressed is needed, which requires proper and adequate corporate disclosure. We advance a legal framework for AI Fairness Reporting to which companies can and should adhere on a comply-or-explain basis. We analyse the sources of unfairness arising from different aspects of AI models and the disparities in the performance of machine learning systems. We evaluate how the machine learning literature has sought to address the problem of unfairness through the use of different fairness metrics. We then put forward a nuanced and viable framework for AI Fairness Reporting comprising: (1) disclosure of all machine learning models usage; (2) disclosure of fairness metrics used and the ensuing trade-offs; (3) disclosure of de-biasing methods used; and (d) release of datasets for public inspection or for third-party audit. We then apply this reporting framework to two case studies.

Contemporary liberal accounts of free expression are almost exclusively preoccupied with the permissible exercises of state power. Influenced by this framing, free expression guarantees under the ECHR, as well as the US and German Constitutions, focus on protecting a private sphere from state interference: what happens within that sphere is only of peripheral concern. This approach is deeply unsatisfactory, especially given the significant threats emanating from private social media platforms that shape the conditions under which individuals may express themselves online. The article argues that we should take private platforms seriously as a source of significant threats, without abandoning the distinction between private actors and the state. Private platforms that are generally open to the public should have obligations to uphold free speech in their contractual relationship to users under certain conditions: if they are structurally dominant, make arbitrary decisions or significantly impact a user's societal participation.