Claudio Novelli

Political parties strive to understand their electorates, and focus groups are a vital tool in these efforts. AI-enhanced simulation technologies (AESTs) enable synthetic focus groups in a fraction of the time (and cost), raising the question of when and how such simulated evidence can be used in campaign research. This paper develops a decision matrix to help party strategists match research needs to appropriate simulation technologies and to identify when to escalate to hybrid or fully human focus groups. The matrix combines three dimensions: strategic purpose, deployment risk, and empirical grounding of the simulation tool. Strategic purpose is the decisive dimension, as it determines what kind of evidence the focus group is meant to produce: observing how political meanings and identities emerge through interaction (Mode 1) or testing and refining campaign messages (Mode 2). The matrix shows that, given documented failure modes such as sycophancy, persona drift, and the suppression of minority viewpoints, AESTs cannot replace human interaction in Mode 1 at any risk level. Within Mode 2, suitability depends instead on deployment risk and on the empirical grounding. Yet even here, we caution that routine reliance on AESTs may erode the qualitative craft on which sound judgment depends.

Deliberative democracy depends on carefully designed institutional frameworks—such as participant selection, facilitation methods, and decision-making mechanisms—that shape how deliberation performs. However, identifying optimal institutional designs for specific contexts remains challenging when relying solely on real-world observations or laboratory experiments: they can be expensive, ethically and methodologically tricky, or too limited in scale to give us clear answers. Computational experiments offer a complementary approach, enabling researchers to conduct large-scale investigations while systematically analyzing complex dynamics, emergent and unexpected collective behavior, and risks or opportunities associated with novel democratic designs. Therefore, this paper explores Digital Twin (DT) technology as a computational testing ground for deliberative systems (with potential applicability to broader institutional analysis). By constructing dynamic models that simulate real-world deliberation, DTs allow researchers and policymakers to rigorously test “what-if” scenarios across diverse institutional configurations in a controlled virtual environment. This approach facilitates evidence-based assessment of novel designs using synthetically generated data, bypassing the constraints of real-world or lab-based experimentation, and without societal disruption. The paper also discusses the limitations of this new methodological approach and suggests where future research should focus.

The emergence of Agentic Artificial Intelligence (AAI) systems capable of independently initiating digital interactions requires a new optimisation paradigm designed explicitly for seamless agent-platform interactions. This article introduces Agentic AI Optimisation (AAIO) as an essential methodology for ensuring effective integration between websites and agentic AI systems. As Search Engine Optimisation (SEO) has shaped digital content discoverability, AAIO can define interactions between autonomous AI agents and online platforms. By examining the mutual interdependence between website optimisation and agentic AI success, the article highlights the virtuous cycle that AAIO can create. It further explores the governance, ethical, legal, and social implications (GELSI) of AAIO, emphasising the need for proactive regulatory frameworks to mitigate potential negative impacts. The article concludes by affirming AAIO’s essential role as part of a fundamental digital infrastructure in the era of autonomous digital agents, advocating equitable and inclusive access to its benefits.

This article examines the Italian legislative proposal to grant legal subjectivity to Lake Garda (DDL 1759, 2026) as a case study in how legal personhood extends to new entities. The article draws on the 'radial conception' of legal personhood, according to which legal personhood has no fixed, mind-independent definition. Instead, the concept is structured around a central case, in many legal systems, the adult citizen of sound mind. This central case results from the convergence of the legally salient cognitive models and produces prototype effects that shape how extension to new cases proceeds. Extensions from the central case are not achieved, though, by logical deduction but through imaginative and analogical reasoning. What matters is whether the extension is persuasive to the legal community that must accept it. Testing the bill against the radial conception, the article finds it well-constructed: e.g., it deploys the right image schemas to make the lake cognitively available as a unified legal subject and grounds the extension in a comparative chain of precedents that reduces its apparent novelty. Where the bill falls short, however, is in leaving unresolved the doctrinal interactions with Italian private and administrative law that a skeptical legal audience will notice first.

This chapter explores the influence of Artificial Intelligence (AI) on digital democracy, focusing on four main areas: citizenship, participation, representation, and the public sphere. It traces the evolution from electronic to virtual and network democracy, underscoring how each stage has broadened democratic engagement through technology. Focusing on digital citizenship, the chapter examines how AI can improve online engagement while posing privacy risks and fostering identity stereotyping. Regarding political participation, it highlights AI's dual role in mobilising civic actions and spreading misinformation. Regarding representation, AI's involvement in electoral processes can enhance voter registration, e-voting, and the efficiency of result tabulation but raises concerns regarding privacy and public trust. Also, AI's predictive capabilities shift the dynamics of political competition, posing ethical questions about manipulation and the legitimacy of democracy. Finally, the chapter examines how integrating AI and digital technologies can facilitate democratic political advocacy and personalised communication. However, this also comes with higher risks of misinformation and targeted propaganda.

Much of the hype about AI exploits the fact that human cognition, language, and categorization rely on imaginative scaffolding. Some degree of metaphor and conceptual borrowing should not be seen as a methodological failure. Law does not meet AI as a blank slate. It reaches for metaphors and other imaginative patterns that make the unfamiliar legible, channeling attention toward certain features and away from others. These are not superficial rhetorical devices, but imaginative patterns that do real work: They frame risks, allocate authority, and steer doctrinal development. The paper uses cognitive linguistics to trace how imaginative patterns structure the regulatory agenda and create path dependence. It concentrates on three especially influential conceptions that circulate across technical, legal, and public discourse: (artificial) intelligence, black box, and hallucinations. “Intelligence” invites personification and prototype effects that lead to misplaced assumptions about agency, even though what is actually being produced is best described as a simulation of a much narrower capacity associated with intelligence: linguistic competence. “Black box” turns a distributed sociotechnical system into a metaphoric bounded container, misleadingly focusing concerns about transparency and responsibility on the model abstracted from its development and implementation. “Hallucinations” imports from psycho-pathology an inappropriate frame which obscures the fact that many errors are foreseeable products of intentional choices. We suggest, instead, reframing them as deferential hazards that arise from design features and expected interactional dynamics.

Recent AI regulations require deployers of high-risk systems to assess impacts on values like fundamental rights and other legally protected interests. However, existing practice-most notably Fundamental Rights Impact Assessments (FRIAs) under the EU AI Actremains mostly limited to qualitative guidelines. As a result, risk evaluation can be inconsistent and highly variable across assessments. To address this issue, we propose a reference model for value-impact assessment that specifies the core components that any more detailed formalization should incorporate. The model builds on a classical risk paradigm, integrating distinct measures for hazard, exposure, vulnerability, and response or mitigation. We use the EU AI Act's FRIA as a reference case for applying the model to AI risk assessments. The model provides three especially salient recommendations. First, it rejects purely ordinal risk matrices in favor of geometric or ratio-scaled severity ladders, enabling the coherent calculation of expected risk. Second, in analyzing risk mitigation measures, it requires separate estimates of efficacy and reliability, with explicit success criteria and scope conditions that allow for dynamic updating. Third, the model operationalizes regulatory precaution, or risk appetite, through the integration of tail-sensitive risk metrics (e.g., Conditional Value-at-Risk). This lets regulators give appropriate weight to low-probability, high-impact (or catastrophic) harms to fundamental rights without distorting ordinary expected-risk calculations. The result is a structured foundation for more consistent value-impact assessments.

This article examines the nature of reasoning in current, mainstream Large Language Models (LLMs) that operate within the token-completion paradigm. We explore their stochastic foundations and phenomenological resemblance to human abductive reasoning. We argue that such LLMs generate text based on learned associations rather than performing abductive inferences. When their output exhibits an apparent abductive quality-often reinforced by interface design-this effect is due to the model's training on human-generated texts that encode reasoning structures. We use some examples to illustrate how LLMs produce plausible hypotheses, simulate commonsense reasoning, and provide explanatory answers without grounding them directly in truth, semantics, verification, or understanding, and without any abductive reasoning. This duality, centred on the stochastic core of the models and the abductive appearance of the applications, has important implications for the evaluation and use of LLMs. They can help generate hypotheses and support human reasoning, but their outputs must be critically examined because they cannot discern truth or verify explanations. In the conclusion, we address five objections to our assertions, some limitations of our analysis, and provide a general assessment.

Recommender Systems (RS) on digital platforms increasingly influence user behavior, raising ethical concerns, privacy risks, harmful content promotion, and diminished user autonomy. This article examines RS within the framework of regulations and lawsuits in the United States and advocates for legislation that can withstand constitutional scrutiny under First Amendment protections. We propose (re)framing RS-curated content as commercial speech, which is subject to lessened free speech protections. This approach provides a practical path for future legislation that would allow for effective oversight of RS, particularly in areas of substantial public interest like child safety, national security, and misinformation.

This article introduces a systematic methodology, called _red reading_, for critical textual analysis that adapts red teaming techniques from computer science and artificial intelligence (AI) into a comprehensive diagnostic protocol, supported by Large Language Models (LLMs), to analyse written documents. As the methodological counterpart of _distant writing_, which transforms text production through AI-assisted design, _red reading_ expands human analytical capabilities and critical reading by embedding adversarial analysis in a constructive framework powered by LLMs. The approach employs a rigorously tested, diagnostic protocol that stress-tests texts across structural, rhetorical, logical, and stylistic dimensions, while strengthening interpretive understanding. The methodology shows how LLM-supported, textual red teaming can provide a rigorous, critical _and_ constructive approach to textual analysis with broad applications across disciplines. We exemplify the method by developing a fifteen-phase diagnostic prompt for red reading. In the Appendix, we apply this protocol to a case study analysing Turing’s seminal article, _Computing Machinery and Intelligence_. We finally discuss the challenges of using red reading and provide some suggestions for future research. In particular, we emphasise the importance of calibration methods for LLMs’ critical analysis, along with evidence-based strategies for seamlessly integrating red reading into real-world workflows.

This paper summarizes the Radial Conception of legal personhood in seven theses. On this view, legal personhood resists checklist definitions; it is a category anchored in a paradigmatic central case formed by the convergence of entrenched (and idealised) models of the world. This structure yields prototype effects, establishing the central case as a cognitive reference point — a default benchmark for reasoning about legal personhood. The salience of the central case — together with the human mind’s imaginative, embodied modes of categorisation — shapes, but does not fix, the motivated pathways by which legal personhood extends to peripheral entities serving analogous institutional functions.

This chapter introduces a conceptual framework for qualitative risk assessment of AI, particularly in the context of the EU AI Act. The framework addresses the complexities of legal compliance and fundamental rights protection by integrating definitional balancing and defeasible reasoning. Definitional balancing employs proportionality analysis to resolve conflicts between competing rights, while defeasible reasoning accommodates the dynamic nature of legal decision-making. Our approach stresses the need for an analysis of AI deployment scenarios and for identifying potential legal violations and multi-layered impacts on fundamental rights. On the basis of this analysis, we provide philosophical foundations for a logical account of AI risk analysis. In particular, we consider the basic building blocks for conceptually grasping the interaction between AI deployment scenarios and fundamental rights, incorporating in defeasible reasoning definitional balancing and arguments about the contextual promotion or demotion of rights. This layered approach allows for more operative models of assessment of both high-risk AI systems and General Purpose AI (GPAI) systems, emphasizing the broader applicability of the latter. Future work aims to develop a formal model and effective algorithms to enhance AI risk assessment, bridging theoretical insights with practical applications to support responsible AI governance.

This article introduces a systematic methodology, called red reading, for critical textual analysis that adapts red teaming techniques from computer science and artificial intelligence (AI) into a comprehensive diagnostic protocol, supported by Large Language Models (LLMs), to analyse written documents. As the methodological counterpart of distant writing, which transforms text production through AI-assisted design, red reading expands human analytical capabilities and critical reading by embedding adversarial analysis in a constructive framework powered by LLMs. The approach employs a rigorously tested, diagnostic protocol that stresstests texts across structural, rhetorical, logical, and stylistic dimensions, while strengthening interpretive understanding. The methodology shows how LLM-supported, textual red teaming can provide a rigorous, critical and constructive approach to textual analysis with broad applications across disciplines. We exemplify the method by developing a fifteen-phase diagnostic prompt for red reading. In the Appendix, we apply this protocol to a case study analysing Turing's seminal article, Computing Machinery and Intelligence. We finally discuss the challenges of using red reading and provide some suggestions for future research. In particular, we emphasise the importance of calibration methods for LLMs' critical analysis, along with evidence-based strategies for seamlessly integrating red reading into real-world workflows.

The proliferation of generative AI systems has created new challenges for the Free and Open Source Software (FOSS) community, particularly regarding how traditional copyleft principles should apply when open source code is used to train AI models. This article introduces the Contextual Copyleft AI (CCAI) license, a novel licensing mechanism that extends copyleft requirements from training data to the resulting generative AI models. The CCAI license offers significant advantages, including enhanced developer control, incentivization of open source AI development, and mitigation of openwashing practices. This is demonstrated through a structured three-part evaluation framework that examines (1) legal feasibility under current copyright law, (2) policy justification comparing traditional software and AI contexts, and (3) synthesis of cross-contextual benefits and risks. However, the increased risk profile of open source AI, particularly the potential for direct misuse, necessitates complementary regulatory approaches to achieve an appropriate risk-benefit balance. The paper concludes that when implemented within a robust regulatory environment focused on responsible AI usage, the CCAI license provides a viable mechanism for preserving and adapting core FOSS principles to the evolving landscape of generative AI development.

Regulating emerging technologies involves balancing the mitigation of risks with the promotion of innovation; a balance frequently seen as a zero-sum "dilemma of control". Regulatory sandboxes offer a practical way to address this dilemma by enabling controlled, evidence-based testing of new technologies. In this article, we examine the regulatory sandbox framework introduced by the EU Artificial Intelligence Act (AIA). We argue that the AIA's multi-level governance structure represents a shift from traditional sandbox models by prioritizing regulatory learning over technological disruption and expanding public interest considerations to include strategically aligned commercial innovations. Afterwards, we identify governance challenges across three sandbox phases-pre-testing, testing, and post-testing-and propose structured solutions. Our analysis suggests that effective sandbox governance requires specific mechanisms: tailored entry criteria, precise pipeline placement guidance, and multi-agency coordination in pre-testing; experimental realism and continuous risk classification updates during testing; and clear graduation criteria with robust transition support in post-testing.

This article examines the feasibility and strategic advantages of developing open-source (OS) foundation models within the European Union (EU). Drawing upon recent developments in AI strategies globally, particularly in China, we argue that the EU’s robust regulatory framework and commitment to ethical principles uniquely position it to produce trustworthy OS foundation models. Due to stringent regulatory compliance, EU-developed OS foundation models would inherently ensure greater trustworthiness, offering a competitive and ethically aligned alternative to proprietary and foreign models. We conclude by offering concrete recommendations to realise this strategic opportunity through EU initiatives such as the AI Factories and EuroHPC infrastructure, emphasising the EU’s potential role in shaping global AI standards.

This article examines potential regulatory pathways for AI in the United States following the Trump administration's 2025 revocation of the Biden-era AI Executive Order. We outline two competing governance scenarios: decentralized state-level regulation (with minimal federal oversight) and centralized federal dominance (through legislative pre-emption). We critically evaluate each model's policy implications, constitutional challenges, and practical trade-offs, particularly regarding innovation and state autonomy. We argue that AI's technological characteristics and context-dependent nature complicate achieving regulatory coherence amid competing federal and state interests. As a result, even under the Trump administration's broader deregulatory agenda, targeted federal intervention may remain necessary.

Deliberative democracy relies on well-designed institutional frameworks-like participant selection, facilitation, and decision-making. Yet identifying the best design for a given context is challenging, as real-world and lab-based studies are often costly, time-consuming, and difficult to replicate. This commentary proposes Digital Twin (DT) technology as a regulatory sandbox for deliberative democracy. By simulating dynamic, data-driven models of real or synthetic communities, DTs allow researchers and policymakers to test alternative designs through controlled "what-if" scenarios, free from real-world constraints. The commentary also examines limitations and key directions for future research.

Deliberative democracy depends on carefully designed institutional frameworks — such as participant selection, facilitation methods, and decision-making mechanisms — that shape how deliberation performs. However, identifying optimal institutional designs for specific contexts remains challenging when relying solely on real-world observations or laboratory experiments: they can be expensive, ethically and methodologically tricky, or too limited in scale to give us clear answers. Computational experiments offer a complementary approach, enabling researchers to conduct large-scale investigations while systematically analyzing complex dynamics, emergent and unexpected collective behavior, and risks or opportunities associated with novel democratic designs. Therefore, this paper explores Digital Twin (DT) technology as a computational testing ground for deliberative systems (with potential applicability to broader institutional analysis). By constructing dynamic models that simulate real-world deliberation, DTs allow researchers and policymakers to rigorously test "what-if" scenarios across diverse institutional configurations in a controlled virtual environment. This approach facilitates evidence-based assessment of novel designs using synthetically generated data, bypassing the constraints of real-world or lab-based experimentation, and without societal disruption. The paper also discusses the limitations of this new methodological approach and suggests where future research should focus.

Recommender Systems (RS) on digital platforms increasingly influence user behavior, raising ethical concerns, privacy risks, harmful content promotion, and diminished user autonomy. This article examines RS within the framework of regulations and lawsuits in the United States and advocates for legislation that can withstand constitutional scrutiny under First Amendment protections. We propose (re)framing RS-curated content as commercial speech, which is subject to lessened free speech protections. This approach provides a practical path for future legislation that would allow for effective oversight of RS, particularly in areas of substantial public interest like child safety, national security, and misinformation.

The EU AI Act is the first step toward a comprehensive legal framework for AI. It introduces provisions for AI systems based on their risk levels in relation to fundamental rights. Providers of AI systems must conduct Conformity Assessments before market placement. Recent amendments added Fundamental Rights Impact Assessments for high-risk AI system users, focusing on compliance with EU and national laws, fundamental rights, and potential impacts on EU values. The paper suggests that automating business process compliance can help standardize these assessments and outlines some methodological guidelines.

L’articolo esamina la proposta europea di regolamento sull’intelligenza artificiale, AI Act (AIA). In particolare, esamina il modello di analisi e valutazione del rischio dei sistemi di IA. L’articolo identifica tre potenziali problemi di implementazione del regolamento: (1) la predeterminazione dei livelli di rischio, (2) la genericità del giudizio di significatività del rischio e (3) l’indeterminatezza della valutazione sull’impatto dei diritti fondamentali. Il saggio suggeriscealcune soluzioni per affrontare questi tre problemi.

This article advances an explanatory model of the academic and policy debate on AI as legal persons. It argues that the scientific and regulatory debate on AI as legal persons undergoes periods of relative stability interrupted by rapid paradigm shifts. Three interrelated factors primarily influence these oscillations: (1) competing theories of legal personhood (clustered versus singularist), (2) capability, embodiment, and commercial reach of AI technology, and (3) AI's integration within socio-digital institutions. Two additional forces modulate the depth and durability of any shift: (4) the overlap and internal consistency of cross‑domain legal regimes (data protection, agency, liability, cybersecurity); and (5) the body of judicial precedents (or their scarcity) on extending personhood. Specific convergences of these factors create pressure for status reconsideration; absent them, equilibrium returns. Current EU law and case practice are explained by partial convergence—capability/diffusion surges and some cluster‑theory uptake without sufficiently embedding AI in rights‑bearing institutional roles—and by the damping effect of dense, interlocking legal regimes, plus scarce and negative judicial precedents. Case law suggests that, without explicit legislation, courts are unlikely to grant AI legal personhood on their own. Prospective movement would require demonstrable de facto social personhood: sustained, role‑based participation within socio-digital institutions that creates governance gaps that existing doctrines cannot internalize. The article concludes by assessing near- and long‑term prospects — from generative AI, autonomous agents, social robots, and digital twins to brain–machine interface — highlighting conditional pathways rather than deterministic trajectories.

This paper reviews the different meanings of vulnerability in the AI Act (AIA). We show that the AIA follows a rather established tradition of looking at vulnerability as a trait or a state of certain individuals and groups. It also includes a promising account of vulnerability as a relation but does not clarify if and how AI changes this relation. We spot the missing piece of the AIA: the lack of recognition that vulnerability is an inherent feature of all human-AI interactions, varying in degree based on design choices and modes of interaction. Finally, we show how such a meaning of vulnerability may be incorporated into the AIA by interpreting the concept of “specific social situation” in Article 5 (b).

The essay analyses the contribution of contemporary analytical metaphysics to socialand legal ontology. In particular, the focus is on two authors: Brian Epstein and JonathanSchaffer. I discuss Epstein’s use of analytical metaphysics notions to explain the structureof social kinds and facts, providing a unique model based on three relations: grounding,anchoring, and framing (GAF).This model offers a new reading of the origin and nature ofsocial entities and brings innovative arguments to the debate in legal ontology. Schaffer’sviews represent a competing thesis, which can be included among the so-called conjunctivisttheories. Epstein and Schaffer’s analyses converge in some respects and both theories appro-ach social and legal ontology with new, fine-grained tools. However, I defend the epistemicand practical value of Epstein’s model. At the same time, I claim that it would bring moreclarity to Epstein’s model if the framing relation was rejected. For this reason, I suggestconsidering a social reality model relying on the grounding-anchoring diagram alone.

Machine unlearning (MU) is often analyzed in terms of how it can facilitate the “right to be forgotten.” In this commentary, we show that MU can support the OECD’s five principles for trustworthy AI, which are influencing AI development and regulation worldwide. This makes it a promising tool to translate AI principles into practice. We also argue that the implementation of MU is not without ethical risks. To address these concerns and amplify the positive impact of MU, we offer policy recommendations across six categories to encourage the research and uptake of this potentially highly influential new technology.

The international network of submarine cables plays a crucial role in facilitating global telecommunications connectivity, carrying over 99% of all internet traffic. However, submarine cables challenge digital sovereignty due to their ownership structure, cross-jurisdictional nature, and vulnerabilities to malicious actors. In this article, we assess these challenges, current policy initiatives designed to mitigate them, and the limitations of these initiatives. The nature of submarine cables curtails a state’s ability to regulate the infrastructure on which it relies, reduces its data security, and threatens its ability to provide telecommunication services. States currently address these challenges through regulatory controls over submarine cables and associated companies, investing in the development of additional cable infrastructure, and implementing physical protection measures for the cables themselves. Despite these efforts, the effectiveness of current mechanisms is hindered by significant obstacles arising from technical limitations and a lack of international coordination on regulation. We conclude by noting how these obstacles lead to gaps in states’ policies and point towards how they could be improved to create a proactive approach to submarine cable governance that defends states’ digital sovereignty.

Regulation by design (RBD) is a growing research field that explores, develops, and criticises the regulative function of design. In this article, we provide a qualitative thematic synthesis of the existing literature. The aim is to explore and analyse RBD’s core features, practices, limitations, and related governance implications. To fulfil this aim, we examine the extant literature on RBD in the context of digital technologies. We start by identifying and structuring the core features of RBD, namely the goals, regulators, regulatees, methods, and technologies. Building on that structure, we distinguish among three types of RBD practices: compliance by design, value creation by design, and optimisation by design. We then explore the challenges and limitations of RBD practices, which stem from risks associated with compliance by design, contextual limitations, or methodological uncertainty. Finally, we examine the governance implications of RBD and outline possible future directions of the research field and its practices.

Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU´s new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative model of governance, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfilment of the legislation. Taken together, the article explores how the AIA may be implemented by national and EU institutional bodies, encompassing longstanding bodies, such as the European Commission, and those newly established under the AIA, such as the AI Office. It investigates their roles across supranational and national levels, emphasizing how EU regulations influence institutional structures and operations. These regulations may not only directly dictate the structural design of institutions but also indirectly request administrative capacities needed to enforce the AIA.