The use of large language models (LLMs) has exploded since November 2022, but there is sparse evidence regarding LLM use in health, medical, and research contexts. We aimed to summarise the current uses of and attitudes towards LLMs across our campus’ clinical, research, and teaching sites. We administered a survey about LLM uses and attitudes. We conducted summary quantitative analysis and inductive qualitative analysis of free text responses. In August–September 2023, we circulated the survey amongst all staff and students across our three campus sites (approximately n = 7500), comprising a paediatric academic hospital, research institute, and paediatric university department. We received 281 anonymous survey responses. We asked about participants’ knowledge of LLMs, their current use of LLMs in professional or learning contexts, and perspectives on possible future uses, opportunities, and risks of LLM use. Over 90% of respondents have heard of LLM tools and about two-thirds have used them in their work on our campus. Respondents reported using LLMs for various uses, including generating or editing text and exploring ideas. Many, but not necessarily all, respondents seem aware of the limitations and potential risks of LLMs, including privacy and security risks. Various respondents expressed enthusiasm about the opportunities of LLM use, including increased efficiency. Our findings show LLM tools are already widely used on our campus. Guidelines and governance are needed to keep up with practice. Insights from this survey were used to develop recommendations for the use of LLMs on our campus.

Quick and accurate forecasts of incidence and mortality trends for the near future are particularly useful for the immediate allocation of available public health resources, as well as for understanding the long-term course of the pandemic. The surveillance data used for predictions, however, may come with some reporting delays. Consequently, auxiliary data sources that are available immediately can provide valuable additional information for recent time periods for which surveillance data have not yet become fully available. In this work, a set of Google search queries by individual users related to COVID-19 incidence and mortality is collected and analyzed. The information from these queries aims to improve quick forecasts. Initially, the identified search query keywords were ranked according to their predictive abilities with reported incidence and mortality. After that, the ARIMA, Prophet, and XGBoost models were fitted to generate forecasts using only the available reported incidence and mortality (baseline model) or together with combinations of searched keywords identified based on their predictive abilities (predictors model). In summary, the inclusion of top-ranked keywords as predictors significantly enhanced prediction accuracy for the majority of scenarios in the range from 50% to 90% across all considered models and is recommended for future use. The inclusion of low-ranked keywords did not provide such an improvement. In general, the ranking of predictors and the corresponding forecast improvements were more pronounced for incidence, while the results were less pronounced for mortality.

The ongoing development of a Swiss Health Data Space (SHDS) presents an opportunity to transform health delivery and care by enabling large-scale secondary health research. The successful implementation of the SHDS depends on its trustworthiness, as public trust is closely linked to public participation in data-sharing initiatives. We conducted four focus groups across the German-, French-, and Italian-speaking regions of Switzerland to identify public expectations and requirements related to the attributes that define a trustworthy SHDS. The participants discussed four fictitious case studies on: (1) consent management; (2) record linkage via the national social security number; (3) national data coordination center; and (4) cross-border data exchange. To best inform Swiss policy, we held a panel discussion with patient experts and healthcare professionals to translate the focus group findings into governance and public communication recommendations. Policy recommendations are proposed based on insights from the fictitious case studies discussed with participants, accompanied by guidance on implementation measures that contribute to proactively building trust in the development of the SHDS. Communication recommendations are further provided, highlighting that the success of the SHDS will depend on early and continuous trustworthy public communication efforts that actively engage the Swiss public, address their concerns, and foster support throughout its development. Overarching these efforts will be a foundational governance approach that meaningfully involves relevant stakeholders and members of the Swiss public, while allocating appropriate responsibility to maintain trustworthiness of the SHDS.

As digital welfare systems expand in local governments worldwide, understanding their implications is crucial for safeguarding public values like transparency, legitimacy, accountability, and privacy. A lack of political debate on data-driven technologies risks eroding democratic legitimacy by obscuring decision-making and impeding accountability mechanisms. In the Netherlands, political discussions on digital welfare within local governments are surprisingly limited, despite evidence of negative impacts on both frontline professionals and citizens. This study examines what mechanisms explain if and how data-driven technologies in the domain of work and income are politically discussed within the municipal government of a large city in the Netherlands, and its consequences. Using a sequential mixed methods design, combining automated text-analysis software ConText (1.2.0) and text-analysis software Atlas.ti (9), we analyzed documents and video recordings of municipal council and committee meetings from 2016 to 2023. Our results show these discussions are rare in the municipal council, occurring primarily either in reaction to scandals, or in reaction to criticism. Two key discursive factors used to justify limited political discussion are: (1) claims of lacking time and knowledge among council members and aldermen, and (2) distancing responsibility and diffusing accountability. This leads to a ‘content chopping’ mechanism, where issues are chopped into small content pieces, for example technical, ethical, and political aspects, and spreading them into separate documents and discussion arenas. This fragmentation can obscure overall coherence and diffuse critical concerns, potentially leading to harmful effects like dehumanization and stereotyping.

National digital ID apps are increasingly gaining popularity globally. As how we transact in the world is increasingly mediated by the digital, questions need to be asked about how these apps support the inclusion of disabled people. In particular, international instruments, such as the United Nations Convention on the Rights of Persons with Disabilities, spotlight the need for inclusive information and communication technologies. In this paper, we adopt a critical disability studies lens to analyse the workings of state-designed digital IDs—Singpass app—and what they can tell us about existing ways of designing for digital inclusion. We situate the case of the Singpass app within the rise of global digital transactions and the political-technical infrastructures that shape their accessibility. We analyse the ways Singpass centres disability, the problems it may still entail, and the possible implications for inclusion. At the same time, we uncover the lessons Singpass’s development holds for questions of global digital inclusion.

This study investigates unintended information flow in large language models (LLMs) by proposing a computational linguistic framework for detecting and analyzing domain anchorage. Domain anchorage is a phenomenon potentially caused by in-context learning or latent “cache” retention of prior inputs, which enables language models to infer and reinforce shared latent concepts across interactions, leading to uniformity in responses that can persist across distinct users or prompts. Using GPT-4 as a case study, our framework systematically quantifies the lexical, syntactic, semantic, and positional similarities between inputs and outputs to detect these domain anchorage effects. We introduce a structured methodology to evaluate the associated risks and highlight the need for robust mitigation strategies. By leveraging domain-aware analysis, this work provides a scalable framework for monitoring information persistence in LLMs, which can inform enterprise guardrails to ensure response consistency, privacy, and safety in real-world deployments.

Australian public sector agencies want to improve access to public sector data to help conduct better informed policy analysis and research and have passed legislation to improve access to this data. Much of this public sector data also contains personal information or health information and is therefore governed by state and federal privacy law which places conditions on the use of personal and health information. This paper therefore analyses how these data sharing laws compare with one another, as well as whether they substantially change the grounds on which public sector data can be shared. It finds that data sharing legislation, by itself, does not substantially change the norms embedded in privacy and health information management law governing the sharing of personal and health information. However, this paper notes that there can still be breaches of social licence even where data sharing occurs lawfully. Further, this paper notes that there are several inconsistencies between data sharing legislation across Australia. This paper therefore proposes reform, policy, and technical strategies to resolve the impact of these inconsistencies.

Since 2017, Digital Twins (DTs) have gained prominence in academic research, with researchers actively conceptualising, prototyping, and implementing DT applications across disciplines. The transformative potential of DTs has also attracted significant private sector investment, leading to substantial advancements in their development. However, their adoption in politics and public administration remains limited. While governments fund extensive DT research, their application in governance is often seen as a long-term prospect rather than an immediate priority, hindering their integration into decision-making and policy implementation. This study bridges the gap between theoretical discussions and practical adoption of DTs in governance. Using the Technology Readiness Level (TRL) and Technology Acceptance Model (TAM) frameworks, we analyse key barriers to adoption, including technological immaturity, limited institutional readiness, and scepticism regarding practical utility. Our research combines a systematic literature review of DT use cases with a case study of Germany, a country characterised by its federal governance structure, strict data privacy regulations, and strong digital innovation agenda. Our findings show that while DTs are widely conceptualised and prototyped in research, their use in governance remains scarce, particularly within federal ministries. Institutional inertia, data privacy concerns, and fragmented governance structures further constrain adoption. We conclude by emphasising the need for targeted pilot projects, clearer governance frameworks, and improved knowledge transfer to integrate DTs into policy planning, crisis management, and data-driven decision-making.

The escalating complexity of global migration patterns renders evident the limitation of traditional reactive governance approaches and the urgent need for anticipatory and forward-thinking strategies. This Special Collection, “Anticipatory Methods in Migration Policy: Forecasting, Foresight, and Other Forward-Looking Methods in Migration Policymaking,” groups scholarly works and practitioners’ contributions dedicated to the state-of-the-art of anticipatory approaches. It showcases significant methodological evolutions, highlighting innovations from advanced quantitative forecasting using Machine Learning to predict displacement, irregular border crossings, and asylum trends, to rich, in-depth insights generated through qualitative foresight, participatory scenario building, and hybrid methodologies that integrate diverse knowledge forms. The contributions collectively emphasize the power of methodological pluralism, address a spectrum of migration drivers, including conflict and climate change, and critically examine the opportunities, ethical imperatives, and governance challenges associated with novel data sources, such as mobile phone data. By focusing on translating predictive insights and foresight into actionable policies and humanitarian action, this collection aims to advance both academic discourse and provide tangible guidance for policymakers and practitioners. It underscores the importance of navigating inherent uncertainties and strengthening ethical frameworks to ensure that innovations in anticipatory migration policy enhance preparedness, resource allocation, and uphold human dignity in an era of increasing global migration.

In many economies, youth unemployment rates over the past two decades have exceeded 10 percentage points, highlighting that not all youth successfully transition successfully from schooling to employment. Equally disturbing are the high rates of young adults not observed in employment, education, or training, a rate commonly referred to as “NEET.” There is not a single pathway for successful transitions. Understanding these pathways and the influences of geographic location, employment opportunities, and family and community characteristics that contribute to positive transitions is crucial. While abundant data exists to support this understanding, it is often siloed and not easily combined to inform schools, communities, and policymakers about effective strategies and necessary changes. Researchers prefer working with datasets, while many stakeholders favor results presented through storytelling and visualizations. This paper introduces YouthView, an innovative online platform designed to provide comprehensive insights into youth transition challenges and opportunities. YouthView integrates information from datasets on youth disadvantage indicators, employment, skills demand, and job vacancy at regional levels. The platform features two modes: a guided storytelling mode with selected visualizations, and an open-ended suite of exploratory dashboards for in-depth data analysis. This dual approach enables policymakers, community organizations, and education providers to gain a nuanced understanding of the challenges faced by different communities. By illuminating spatial patterns, socioeconomic disparities, and relationships between disadvantage factors and labor market dynamics, YouthView facilitates informed decision-making and the development of targeted interventions, ultimately contributing to improved youth economic outcomes and expanded opportunities in areas of greatest need.

Embracing the potential of foresight in migration policy, North Macedonia has embarked on a ground-breaking journey to institutionalize anticipatory governance through extensive capacity-building activities, imparting foresight methods to stakeholders responsible for shaping migration policies. This research provides a comprehensive overview, detailing the initiative’s origins, alignment with the Resolution on Migration Policy 2021–2025, and the accompanying Action Plan. The study assesses the impact and potential of the Anticipatory Governance in Migration in North Macedonia when fully integrated with the action plan, which focuses on data-based management that oversees the migration policy resolution and the migration policy milieu. Through a comprehensive analysis of the foresight interventions, training programs, and stakeholder engagements, this study unveils the potential impact of forward-looking planning on North Macedonia’s migration policy landscape. The conclusion and recommendations have broader significance, extending beyond North Macedonia to serve as a model for other countries confronting migration challenges in our rapidly changing world.

There is a growing attention towards personalised digital health interventions such as health apps. These often depend on the collection of sensitive personal data, which users generally have limited control over. This work explores perspectives on data sharing and health apps in two different policy contexts, London and Hong Kong. Through this study, our goal is to generate insight about what digital health futures should look like and what needs to be done to achieve them. Using a survey based on a hypothetical health app, we considered a range of behavioural influences on personal health data sharing with the Capability, Opportunity, Motivation model of Behaviour (COM-B) to explore some of the key factors affecting the acceptability of data sharing. Results indicate that willingness to use health apps is influenced by users’ data literacy and control, comfort with sharing health and location data, existing health concerns, access to personalised health advice from a trusted source, and willingness to provide data access to specific parties. Gender is a statistically significant factor, as men are more willing to use health apps. Survey respondents in London are statistically more willing to use health apps than respondents in Hong Kong. Finally, we propose several policy approaches to address these factors, which include the co-creation of standards for using artificial intelligence (AI) to generate health advice, innovating app design and governance models that allow users to carefully control their data, and addressing concerns of gender-specific privacy risks and public trust in institutions dealing with data.

The integration of artificial intelligence (AI)-driven technologies into peace dialogues offers both innovative possibilities and critical challenges for contemporary peacebuilding practice. This article proposes a context-sensitive taxonomy of digital deliberation tools designed to guide the selection and adaptation of AI-assisted platforms in conflict-affected environments. Moving beyond static typologies, the framework accounts for variables such as scale, digital literacy, inclusivity, security, and the depth of AI integration. By situating digital peace dialogues within broader peacebuilding and digital democracy frameworks, the article examines how AI can enhance participation, scale deliberation, and support knowledge synthesis, —while also highlighting emerging concerns around algorithmic bias, digital exclusion, and cybersecurity threats. Drawing on case studies involving the United Nations (UN) and civil society actors, the article underscores the limitations of one-size-fits-all approaches and makes the case for hybrid models that balance AI capabilities with human facilitation to foster trust, legitimacy, and context-responsive dialogue. The analysis contributes to peacebuilding scholarship by engaging with the ethics of AI, the politics of digital diplomacy, and the sustainability of technological interventions in peace processes. Ultimately, the study argues for a dynamic, adaptive approach to AI integration, continuously attuned to the ethical, political, and socio-cultural dimensions of peacebuilding practice.

The emergence of large language models has significantly expanded the use of natural language processing (NLP), even as it has heightened exposure to adversarial threats. We present an overview of adversarial NLP with an emphasis on challenges, policy implications, emerging areas, and future directions. First, we review attack methods and evaluate the vulnerabilities of popular NLP models. Then, we review defense strategies that include adversarial training. We describe major policy implications, identify key trends, and suggest future directions, such as the use of Bayesian methods to improve the security and robustness of NLP systems.

European asylum policy still has a long way to go to better address protection challenges. This paper presents data and visualizations that should help improve responsibility-sharing and solidarity between states. We developed an interactive cartographic tool to map the distribution of refugees in Europe. Besides the observed geographic distribution of asylum seekers and beneficiaries of the temporary protection status, our tool allows for the calculation of a theoretical distribution between countries based on different criteria. The tool is an interactive visualization created with the software “Tableau Desktop.” The original data was collected from Eurostat and the World Bank, before being processed by the research team with the Extract Transform Load (ETL) utility “Tableau Prep” and made available through the Tableau Desktop application. The actual number of asylum applications lodged in country A can thus be compared with the number that would be proportional to that country’s population within Europe in combination with three other criteria. Maps of observed and theoretical reallocations can thus be produced based on population size, area, unemployment rate, economic prosperity or a mix of these factors. The number of refugees received is represented by a red semicircle while the “equitable” number in proportion to given criteria is represented by a grey semicircle. Our database not only allows geographical analysis of the drivers of refugee distribution in Europe, but it also provides the population and policymakers with a solid basis for discussing responsibility-sharing schemes, such as those envisaged in the new EU Asylum Pact of 2024.

As social media continues to grow, understanding the impact of storytelling on stakeholder engagement becomes increasingly important for policymakers and organizations who wish to influence policymaking. While prior research has explored narrative strategies in advertising and branding, researchers have paid scant attention to the specific influence of stories on social media stakeholder engagement. This study addresses this gap by employing Narrative Transportation Theory (NTT) and leveraging Natural Language Processing (NLP) to analyze the intricate textual data generated by social media platforms. The analysis of 85,075 Facebook publications from leading Canadian manufacturing companies, using Spearman’s rank correlation coefficient, underscores that individual storytelling components—character, sequence of events, and setting—along with the composite narrative structure significantly enhance stakeholder engagement. This research contributes to a deeper understanding of storytelling dynamics in social media, emphasizing the importance of crafting compelling stories to drive meaningful stakeholder engagement in the digital realm. The results of our research can prove useful for those who wish to influence policymakers or for policymakers who want to promote new policies.

Over 193 countries have signed at least one of more than 500 multilateral treaties addressing critical global issues, such as human rights, environmental protection, and trade. Ratifying a treaty obligates a country, as a “State Party,” to report to the United Nations on its progress toward implementing the treaty’s provisions. These reports and their associated review processes generate a wealth of textual data. Effectively monitoring, reviewing, and assessing national, regional, and global progress toward these treaty commitments is crucial for ensuring compliance and realizing the benefits of international cooperation. The UN Convention on the Rights of Persons with Disabilities (CRPD), which has been ratified by 191 countries, exemplifies this challenge. With over 1.3 billion people worldwide living with disabilities, the CRPD aims to promote a shift from a charity-based “medical model” that views disability as an individual deficiency, to a rights-based “social justice model” that emphasizes societal barriers and inclusivity. Each State Party submits periodic reports to the Committee on the Rights of Persons with Disabilities detailing their implementation efforts. This study analyzed all available CRPD State Reports (N = 170) using text mining, Natural Language Processing, and GenerativeAI tools to assess global progress, identify regional variations, and explore the factors influencing successful implementation. The findings reveal evidence of widespread CRPD implementation, growing support for social justice and economic inclusion, and the importance of civil society engagement. Hybrid data analysis approach of this study offers a promising framework for harnessing the power of textual data to advance the realization of treaty commitments worldwide.

In our digital world, reusing data to inform: decisions, advance science, and improve people’s lives should be easier than ever. However, the reuse of data remains limited, complex, and challenging. Some of this complexity requires rethinking consent and public participation processes about it. First, to ensure the legitimacy of uses, including normative aspects like agency and data sovereignty. Second, to enhance data quality and mitigate risks, especially since data are proxies that can misrepresent realities or be oblivious to the original context or use purpose. Third, because data, both as a good and infrastructure, are the building blocks of both technologies and knowledge of public interest that can help societies work towards the well-being of their people and the environment. Using the case study of the European Health Data Space, we propose a multidimensional, polytopic framework with multiple intersections to democratising decision-making and improving the way in which meaningful participation and consent processes are conducted at various levels and from the point of view of institutions, regulations, and practices.

Although ‘in-the-wild’ technology testing provides an important opportunity to collect evidence about the performance of new technologies in real world deployment environments, such tests may themselves cause harm and wrongfully interfere with the rights of others. This paper critically examines real-world AI testing, focusing on live facial recognition technology (FRT) trials by European law enforcement agencies (in London, Wales, Berlin, and Nice) undertaken between 2016 and 2020, which serve as a set of comparative case studies. We argue that there is an urgent need for a clear framework of principles to govern real-world AI testing, which is currently a largely ungoverned ‘wild west’ without adequate safeguards or oversight. We propose a principled framework to ensure that these tests are undertaken in an epistemically, ethically, and legally responsible manner, thereby helping to ensure that such tests generate sound, reliable evidence while safeguarding the human rights and other vital interests of others. Although the case studies of FRT testing were undertaken prior to the passage of the EU’s AI Act, we suggest that these three kinds of responsibility should provide the foundational anchor points to inform the design and conduct of real-world testing of high-risk AI systems pursuant to Article 60 of the AI Act.