Artificial intelligence tools for citizen participation have been widely promoted as innovations that can make democratic decision-making more inclusive, efficient, and responsive. Much of the existing debate concentrates on the technical affordances of these tools and the possibilities they create under ideal conditions. While valuable, this focus has obscured a crucial question: who builds, funds, and adopts such tools in practice? We argue that a political economy perspective is necessary to understand the conditions under which AI for citizen participation can meaningfully contribute to democratic governance and how this proposed future may unfold in practice. Drawing on desk research, our experience in relevant research, practice, and policy communities, and informal interviews, we propose a heuristic framework that identifies archetypes of organisations building tools, the funding models that shape their incentives, and the adoption pathways that condition their use. This approach highlights the trade-offs, constraints, and dynamics that influence which tools persist and scale. We suggest that policymakers should not only ask what kinds of tools to adopt but also how to shape an ecosystem that sustains diverse, innovative, and democratically oriented approaches. Our analysis provides an ex-ante framework for situating emerging practices and identifying policy levers to help ensure that AI tools for citizen participation serve the public good.

This article examines how artificial intelligence (AI) impacts state sovereignty and the balance between innovation and control in AI governance through a case study of Türkiye. As AI technologies become increasingly sophisticated, they challenge traditional notions of sovereignty, creating tensions between fostering innovation and maintaining regulatory control. The concept of “AI sovereignty” encompasses a state’s ability to exercise meaningful control over AI infrastructure, data resources, regulatory frameworks, and technological capabilities. Türkiye’s AI strategy illustrates how “middle powers” navigate this balance, especially as it stands at the crossroads of Europe and Asia, requiring Türkiye to develop distinct approaches that reflect its unique geopolitical position. The analysis reveals that sovereignty in the AI domain encompasses multiple dimensions—technical capabilities, regulatory frameworks, and strategic positioning—requiring adaptable governance approaches. The findings offer insights for jurisdictions seeking to balance innovation imperatives with control mechanisms while maintaining strategic autonomy in an evolving global AI landscape.

This article addresses the challenges in formulating data-sharing regulations by proposing a systematic regulatory matrix based on data characteristics. While data-driven innovation drives economic growth, existing legal frameworks in the EU are incoherent and often tend towards data propertisation, even if indirectly, which may lead to data underutilisation. The matrix is built on varied data characteristics, and it aims to foster access to data and data sharing as a fundamental general principle underpinning the data-driven innovation market. This framework offers a balanced regulatory scheme ranging from open access to proprietary models, aiming to maximise innovation and public good in the emerging field of data law.

The paper presents a framework to automatically identify crack patterns and the related features in existing reinforced concrete (RC) bridges. The challenge of this work is to define a tool for detecting the focused defect and highlighting the number and the orientation of cracks, allowing for correct interpretation and driving further evaluations on the residual life of the structure. The study is framed within the increasing interest in monitoring the structural health of existing bridges through automated tools, able to support engineers in the phase of visual assessment and interpretation of structural defects. When dealing with periodic inspection of large bridge portfolios, the support provided by automated tools can be fundamental for planning further strategies aimed at ensuring the structural safety and preventing future disasters. Given a stack of photos of a bridge structural element, an image stitching procedure is proposed to produce a near-complete image of the entire element. On the latter, a pipeline of deep-learning (DL) algorithms is employed to automatically detect and identify cracks (as a combination of object detection and segmentation algorithms). Finally, the proposed tool extracts cracks for counting and defines their orientation (i.e., vertical, horizontal, diagonal), in order to provide near-complete information about the crack pattern for the structural element. A full description of the methodology and the proposed algorithms is reported throughout the manuscript, showing the main pros and cons and assessing the effectiveness of the tool on a real-life case study.

This document presents some advances in personalised and precision nutrition that were addressed in the IUNS-ICN 2025 Congress at August 2025 including the role of biomarkers in personalised and precision nutrition research, personalised and precision nutrition approaches to routine health care services, implications for public health, personalised and precision nutrition around the world, machine learning and precision nutrition, and personalised dietary guidelines alongside population-based policies.

Digital policymaking in the European Union (EU), once seen as an internal market concern, is increasingly shaped by non-economic aims, such as the pursuit of security and the protection of fundamental rights. Recent pieces of legislation, such as the AI Act or the Cyber Resilience Act, have nominally acknowledged the relevance of such factors, but serious concerns have been raised about security considerations de facto trumping all others. In this article, we argue that, despite its predominance, security does not displace fundamental rights or the internal market as the foundations of EU digital law. Instead, we propose a framework to explain how the interaction among rationales for security promotion, rights protection, and market-making goes beyond mere opposition. Applying this framework to three case studies of post-GDPR regulation, we show that the deepening of fundamental rights safeguards in digital regulatory instruments offers, at most, a limited check to creeping securitisation – and sometimes even allows the EU legislator to extend the reach of security measures in the name of protecting certain rights. Understanding the logics and actors that shape the triple helix of markets, rights, and security is therefore crucial for properly understanding – and responding to – security overreach in cyberspace.

The Health Technology Assessment International (HTAi) 2025 annual meeting featured three main plenaries to explore next-generation (NextGen) evidence in health technology assessment (HTA). In this commentary, we capture the discussions of Plenary 2: NextGen Methods: Hype or Here to Stay? Each plenary panelist was tasked to convincingly debate the need, rigor, and implementability of one of three emerging method domains in HTA: (1) Environmental Sustainability, (2) Adaptive HTA, and (3) Artificial Intelligence (AI)-enabled Real-World Evidence (RWE). The three panelists convincingly debated that their method would endure beyond initial hype; all three methods were perceived to have a moderate to high level of need, rigor, and implementability by the audience. Key questions from the audience included a request for examples of where HTA reviews have considered environmental sustainability, a challenge for adaptive HTA to embrace other value elements outside of cost-effectiveness, and a question about how the human-in-the-loop principle fits into AI-driven RWE and what this means for HTA agencies that are already stretched for resources. In this commentary, we summarize the presentations, discussions, and audience engagement to provide readers with accessible insight into the debate about which method(s) are anticipated to endure beyond their initial hype.

This article examines how artificial intelligence (AI) became framed as a critical node of U.S.–China strategic competition between 2015 and 2023, arguing that “hybrid epistemic experts” – figures who straddle technical expertise, corporate leadership and policy influence – played a decisive role in shaping elite understanding of AI. Through a critical review of policy documents, news media, public statements and institutional developments, this article examines how the “U.S.–China AI Race” narrative did not emerge along the usual pathways of state-driven, top-down bureaucratic processes or traditional lobbying but was actively constructed and amplified by figures like former Google executive Eric Schmidt. Schmidt’s role as a hybrid actor allowed him to translate AI from a narrow technological domain into an existential competition requiring massive policy investment. This overarching capability was driven by AI’s speculative, technically complex and general-purpose nature, which has concentrated knowledge production in private hands, enabling hybrid actors to achieve disproportionate influence over AI policy discourse. This phenomenon raises concerns about democratic governance, the collapse of independent expertise and the self-reinforcing dynamics between private power and public policymaking in emerging technologies.

This paper examines how the United Arab Emirates (UAE) and Kingdom of Saudi Arabia (KSA) are emerging as pivotal actors in the global race for frontier AI dominance, and analyzes the implications for U.S. strategic interests. It evaluates each country’s current position in the frontier AI development and deployment supply chain – detailing the UAE’s and KSA’s massive investments in AI infrastructure and connections with the USA, China and France – as well as the two Gulf states’ unique advantages in capital, energy and centralized governance. Gulf investments are reshaping global tech supply chains and could either strengthen or undermine U.S. technological leadership, depending on U.S. engagement. The paper recommends a proactive U.S. strategy to leverage Gulf AI ambitions while safeguarding national security. Recommendations include enforcing rigorous technical safeguards on Gulf-based AI infrastructure, tightening export control oversight to prevent diversion of advanced chips, joint targeted R&D investment initiatives, co-development of international AI standards, strict investment screening via the Committee on Foreign Investment in the USA and measures to prevent conflicts of interest.

Artificial Intelligence (AI) systems are increasingly supporting targeting, intelligence analysis and operational planning across military domains, reshaping how commanders use force through human–machine teaming (HMT). HMT offers operational advantages, but risks such as automation bias, adversarial manipulation and degraded performance pose challenges for the ability of deployers to use AI systems in compliance with international humanitarian law (IHL). This article argues that adherence to IHL cannot be deferred until hostilities arise; system design, testing, governance and training must be structured in advance to establish the conditions under which AI-enabled human–machine teams can exercise appropriate human judgement consistent with IHL obligations. The paper proposes an HMT Assurance Card, a cross-disciplinary life-cycle instrument integrating IHL obligations, civilian harm pattern analysis and AI governance frameworks into measurable standards applicable across the AI system life cycle, defence institutions and coalition environments.

Suicide is a significant global public health concern, and conventional clinical risk assessment is constrained by workforce availability, scalability, and clinician variability. The classic suicidality risk evaluation is largely dependent on clinical judgment, which, although helpful, can demonstrate a ceiling effect in its predictive validity. AI-driven chatbots, conversational systems that engage users in real-time natural language dialog, have emerged as candidate tools for augmenting suicide risk detection and prevention. This narrative review aimed to: evaluate the performance of AI-driven chatbots in detecting and assessing suicidal ideation relative to clinical benchmarks; examine the effectiveness of chatbot-based interventions for suicide prevention; and identify ethical, cultural, and implementation challenges limiting clinical translation. Six electronic databases were searched, with the initial search conducted in 2024 and updated in 2026 through targeted monitoring, with no upper cutoff date applied. A thematic narrative synthesis approach was applied across five domains. Eleven primary studies met eligibility criteria and were included in the synthesis. Chatbot-based risk assessment showed adequate response alignment with expert judgment at the extremes of suicide risk, but consistently failed to distinguish intermediate risk levels across multiple model families. Across 29 tested commercial chatbot agents, none met the criteria for an adequate suicidal crisis response. A clinically designed, framework-anchored chatbot achieved high efficacy across six outcome domains. Three percent of social chatbot users reported halted suicidal ideation, and a purpose-built clinical chatbot in emergency department settings significantly improved evidence-based care delivery. Systematic risk severity underestimation and the absence of cross-cultural evaluation were identified across all studies. AI-driven chatbots show potential as adjunctive tools across the suicide care continuum. Clinically designed, evidence-anchored chatbots demonstrate feasibility and meaningful benefit; commercially deployed chatbots without clinical validation demonstrate near-universal crisis response inadequacy. Mandatory clinical validation prior to public release, clinician oversight, and crisis system integration are prerequisites for responsible deployment.

This paper describes a prototype test harness that has been developed to evaluate the use of large language model retrieval-augmented generation systems for humanities and social science research, with an emphasis on collections held in the galleries, libraries, archives and museum sector. We propose the development of new modes of research software engineering oriented towards the values and principles of those communities, and rigorous modes of software testing grounded in industry best practices.