Within the context of the Reg. (EU) 2024/1689, also known as Artificial Intelligence Act (AI Act), this paper aims at proposing a methodology for conducting the Fundamental Rights Impact Assessment (FRIA). Bridging the gap between regulatory obligations and practical implementation, the paper presents a systematic approach to assess and mitigate risks to fundamental rights (FR), through a combined qualitative and semi-quantitative methodology. Specifically, recognising the necessary intersection between law and computer science, this research addresses the challenges posed by high-risk AI systems (Art. 6, Annex III AI Act), with a focus on Generative AI (GenAI) components. While the first part of the paper situates the FRIA within the broader European legal and constitutional framework, the second part delves more into the details of the proposed methodology. This latter one is composed of two complementary tools, namely, a comprehensive questionnaire – aimed at gathering contextual and technical data about the AI system – and a risk assessment matrix, which is used to quantify the potential impacts on FR. When used together, these instruments enable a comprehensive assessment of the overall risk posed by GenAI systems. The methodology is then applied to a use case involving a GenAI system deployed in high-risk AI contexts, aiming to map the specific risks on FR associated with these technologies. Finally, the paper concludes by stressing the framework potential to enable providers and deployers to implement responsible AI practices, thus strengthening public trust in AI technologies and aligning with the European Union’s values.

The nuclear shell model is one of the most adopted many-body methods for the description of atomic nuclei whose main ingredient is the effective Hamiltonian. One of the approaches widely used to derive it is of phenomenological type, where its matrix elements are considered parameters to be fixed to reproduce the experimental data. However, the number of parameters as well as the number of experimental data dramatically increases with the mass of the nuclei under investigation and the commonly adopted procedures such as least-square fitting become computationally prohibitive. Therefore, there is a strong emergence in finding alternative approaches to construct effective Hamiltonians for heavy nuclei. To pave the way in this direction, the proposed work exploits for the very first time Genetic Algorithms. Indeed, their ability to simultaneously examine and manipulate sets of possible solutions could be crucial to deal with the above issues. The suitability of Genetic Algorithms in computing effective Hamiltonians is evaluated experimentally for the p - shell interaction, where the obtained results, without any physical constraint on the parameters, outperform the current widely-adopted description represented by the Cohen and Kurath solution.

Online platforms provide primary points of access to information and other content in the digital age. They foster users’ ability to share ideas and opinions while offering opportunities for cultural and creative industries. In Europe, ownership and use of such expressions is partly governed by a complex web of legislation, sectoral self- and co-regulatory norms. To an important degree, it is also governed by private norms defined by contractual agreements and informal relationships between users and platforms. By adopting policies usually defined as Terms of Service and Community Guidelines, platforms almost unilaterally set use, moderation and enforcement rules, structures and practices (including through algorithmic systems) that govern the access and dissemination of protected content by their users. This private governance of essential means of access, dissemination and expression to (and through) creative content is hardly equitable, though. In fact, it is an expression of how platforms control what users – including users-creators – can say and disseminate online, and how they can monetise their content. As platform power grows, EU law is adjusting by moving towards enhancing the responsibility of platforms for content they host. One crucial example of this is Article 17 of the new Copyright Directive (2019/790), which fundamentally changes the regime and liability of “online content-sharing service providers” (OCSSPs). This complex regime, complemented by rules in the Digital Services Act, sets out a new environment for OCSSPs to design and carry out content moderation, as well as to define their contractual relationship with users, including creators. The latter relationship is characterized by significant power imbalance in favour of platforms, calling into question whether the law can and should do more to protect users-creators. This article addresses the power of large-scale platforms in EU law over their users’ copyright-protected content and its effects on the governance of that content, including on its exploitation and some of its implications for freedom of expression. Our analysis combines legal and empirical methods. We carry our doctrinal legal research to clarify the complex legal regime that governs platforms’ contractual obligations to users and content moderation activities, including the space available for private ordering, with a focus on EU law. From the empirical perspective, we conducted a thematic analysis of most versions of the Terms of Services published over time by the three largest social media platforms in number of users – Facebook, Instagram and YouTube – so as to identify and examine the rules these companies have established to regulate user-generated content, and the ways in which such provisions shifted in the past two decades. In so doing, we unveil how foundational this sort of regulation has always been to platforms’ functioning and how it contributes to defining a system of content exploitation.