This article focuses on copyright issues pertaining to generative artificial intelligence (AI) systems, with particular emphasis on the ChatGPT case study as a primary exemplar. In order to generate high-quality outcomes, generative AI systems require substantial quantities of training data, which may frequently comprise copyright-protected information. This prompts inquiries into the legal principles of fair use, the creation of derivative works and the lawfulness of data gathering and utilisation. The utilisation of input data for the purpose of training and enhancing AI models presents significant concerns regarding potential violations of copyright. This paper offers suggestions for safeguarding the interests of copyright holders and competitors, while simultaneously addressing legal challenges and expediting the advancement of AI technologies. This study analyses the ChatGPT platform as a case example to explore the necessary modifications that copyright regulations must undergo to adequately tackle the intricacies of authorship and ownership in the realm of AI-generated creative content.

Artificial intelligence (AI) is becoming a part of our daily lives at a fast pace, offering myriad benefits for society. At the same time, there is concern about the unpredictability and uncontrollability of AI. In response, legislators and scholars call for more transparency and explainability of AI. This article considers what it would mean to require transparency of AI. It advocates looking beyond the opaque concept of AI, focusing on the concrete risks and biases of its underlying technology: machine-learning algorithms. The article discusses the biases that algorithms may produce through the input data, the testing of the algorithm and the decision model. Any transparency requirement for algorithms should result in explanations of these biases that are both understandable for the prospective recipients, and technically feasible for producers. Before asking how much transparency the law should require from algorithms, we should therefore consider if the explanation that programmers could offer is useful in specific legal contexts.

The proposed Artificial Intelligence Act (AI Act) is the first comprehensive attempt to regulate artificial intelligence (AI) in a major jurisdiction. This article analyses Article 9, the key risk management provision in the AI Act. It gives an overview of the regulatory concept behind the norm, determines its purpose and scope of application, offers a comprehensive interpretation of the specific risk management requirements and outlines ways in which the requirements can be enforced. This article can help providers of high-risk systems to comply with the requirements set out in Article 9. In addition, it can inform revisions of the current draft of the AI Act and efforts to develop harmonised standards on AI risk management.

In Nudge (2008) Richard Thaler and Cass Sunstein suggested that public policy–makers arrange decision–making contexts in ways to promote behaviour change in the interest of individual citizens as well as that of society. However, in the public sphere and Academia alike widespread discussions have appeared concerning the public acceptability of nudgebased behavioural policy. Thaler and Sunstein's own position is that the anti–nudge position is a literal non–starter, because citizens are always influenced by the decision making context anyway, and nudging is liberty preserving and acceptable if guided by Libertarian Paternalism and Rawls’ publicity principle. A persistent and central tenet in the criticism disputing the acceptability of the approach is that nudging works by manipulating citizens’ choices. In this paper, we argue that both lines of argumentation are seriously flawed. We show how the anti–nudge position is not a literal non–starter due to the responsibilities that accrue on policy–makers by the intentional intervention in citizens’ life, how nudging is not essentially liberty preserving and why the approach is not necessarily acceptable even if satisfying Rawls’ publicity principle. We then use the psychological dual process theory underlying the approach as well as an epistemic transparency criterion identified by Thaler and Sunstein themselves to show that nudging is not necessarily about “manipulation”, nor necessarily about influencing “choice”. The result is a framework identifying four types of nudges that may be used to provide a central component for more nuanced normative considerations as well as a basis for policy recommendations.

On 16 December 2020, the European Commission delivered on the plans proposed in the European Digital Strategy by publishing two proposals related to the governance of digital services in the European Union: the Digital Services Act (DSA) and the Digital Markets Act (DMA). The much-awaited regulatory reform is often mentioned in the context of content moderation and freedom of expression, market power and competition. It is, however, important to bear in mind the contractual nature of the relationship between users and platforms and the additional contracts concluded on the platform between the users, in particular traders and consumers. Moreover, the monetisation offered by digital platforms has led to new dynamics and economic interests. This paper explores the reform proposed by the European Commission by means of the DSA by touching upon four main themes that will be addressed from the perspective of consumer protection: (1) the internal coherence of European Union law; (2) intermediary liability; (3) the outsourcing of solutions to private parties; and (4) digital enforcement.

How can tort law contribute to a better understanding of the risk-based approach in the European Union’s (EU) Artificial Intelligence Act proposal and evolving liability regime? In a new legal area of intense development, it is pivotal to make the best use possible of existing regulation and legal knowledge. The main objective of this article is thus to investigate the relationship between traditional tort law principles, with a focus on risk assessments, and the developing legislation on artificial intelligence (AI) in the EU. The article offers a critical analysis and evaluation from a tort law perspective of the risk-based approach in the proposed AI Act and the European Parliament resolution on a civil liability regime for AI, with comparisons also to the proposal for a revised and AI-adapted product liability directive and the recently proposed directive on civil liability for AI. The discussion leads to the illumination of both challenges and possibilities in the interplay between AI, tort law and the concept of risk, displaying the large potential of tort law as a tool for handling rising AI issues.

Governments around the world are strictly regulating information on social media in the interests of addressing fake news. There is, however, a risk that the uncontrolled spread of information could increase the adverse effects of the COVID-19 health emergency through the influence of false and misleading news. Yet governments may well use health emergency regulation as a pretext for implementing draconian restrictions on the right to freedom of expression, as well as increasing social media censorship (ie chilling effects). This article seeks to challenge the stringent legislative and administrative measures governments have recently put in place in order to analyse their negative implications for the right to freedom of expression and to suggest different regulatory approaches in the context of public law. These controversial government policies are discussed in order to clarify why freedom of expression cannot be allowed to be jeopardised in the process of trying to manage fake news. Firstly, an analysis of the legal definition of fake news in academia is presented in order to establish the essential characteristics of the phenomenon (Section II). Secondly, the legislative and administrative measures implemented by governments at both international (Section III) and European Union (EU) levels (Section IV) are assessed, showing how they may undermine a core human right by curtailing freedom of expression. Then, starting from the premise of social media as a “watchdog” of democracy and moving on to the contention that fake news is a phenomenon of “mature” democracy, the article argues that public law already protects freedom of expression and ensures its effectiveness at the international and EU levels through some fundamental rules (Section V). There follows a discussion of the key regulatory approaches, and, as alternatives to government intervention, self-regulation and especially empowering users are proposed as strategies to effectively manage fake news by mitigating the risks of undue interference by regulators in the right to freedom of expression (Section VI). The article concludes by offering some remarks on the proposed solution and in particular by recommending the implementation of reliability ratings on social media platforms (Section VII).

Until robots and humans mostly worked in fast-paced and yet separate environments, occupational health and safety (OHS) rules could address workers’ safety largely independently from robotic conduct. This is no longer the case: collaborative robots (cobots) working alongside humans warrant the design of policies ensuring the safety of both humans and robots at once, within shared spaces and upon delivery of cooperative workflows. Within the European Union (EU), the applicable regulatory framework stands at the intersection between international industry standards and legislation at the EU as well as Member State level. Not only do current standards and laws fail to satisfactorily attend to the physical and mental health challenges prompted by human–robot interaction (HRI), but they exhibit important gaps in relation to smart cobots (“SmaCobs”) more specifically. In fact, SmaCobs combine the black-box unforeseeability afforded by machine learning with more general HRI-associated risks, towards increasingly complex, mobile and interconnected operational interfaces and production chains. Against this backdrop, based on productivity and health motivations, we urge the encoding of the enforcement of OHS policies directly into SmaCobs. First, SmaCobs could harness the sophistication of quantum computing to adapt a tangled normative architecture in a responsive manner to the contingent needs of each situation. Second, entrusting them with OHS enforcement vis-à-vis both themselves and humans may paradoxically prove safer as well as more cost-effective than for humans to do so. This scenario raises profound legal, ethical and somewhat philosophical concerns around SmaCobs’ legal personality, the apportionment of liability and algorithmic explainability. The first systematic proposal to tackle such questions is henceforth formulated. For the EU, we propose that this is achieved through a new binding OHS Regulation aimed at the SmaCobs age.

This paper argues for a sandbox approach to regulating artificial intelligence (AI) to complement a strict liability regime. The authors argue that sandbox regulation is an appropriate complement to a strict liability approach, given the need to maintain a balance between a regulatory approach that aims to protect people and society on the one hand and to foster innovation due to the constant and rapid developments in the AI field on the other. The authors analyse the benefits of sandbox regulation when used as a supplement to a strict liability regime, which by itself creates a chilling effect on AI innovation, especially for small and medium-sized enterprises. The authors propose a regulatory safe space in the AI sector through sandbox regulation, an idea already embraced by European Union regulators and where AI products and services can be tested within safeguards.