School shootings remain a prominent threat in the United States, with a dramatic increase in the number of incidents since 2021. Understanding the factors that distinguish averted school shootings from those that are carried out is critical for improving prevention strategies and policies. This study analyzes 278 incidents from the Averted School Violence database from Safe and Sound Schools to identify factors of whether a school shooting was averted or carried out. Logistic regression models revealed that incidents had higher odds of being carried out when the shooter had experienced a traumatic life event or had a co-conspirator. Schools with active shooter response protocols were associated with higher odds of averted incidents, while those with enhanced security measures had higher odds to be carried-out shootings. These findings highlight the importance of integrating mental health support, behavioral monitoring, and strategic school safety policies to reduce the likelihood of school shootings and foster safer educational environments.

This article examines the future of the world economy from the standpoint of middle powers, with particular attention to Canada in the wake of Prime Minister Mark Carney’s renewed emphasis on middle-power coordination. It argues that the central economic transformation is not simply digitization, but a deeper pivot from the production of goods and knowledge toward the valuation of data generated through those production processes. Agriculture, manufacturing, health, education, logistics, and public administration are increasingly organized around data capture, platform intermediation, and AI-enabled recombination. This shift intensifies economies of scale, network effects, and economies of scope, thereby concentrating economic power in a small group of multinational technology firms. For middle powers, the resulting challenge is not to reject global platforms, but to avoid operational dependency on them. The article advances digital sovereignty as a framework based on control, interoperability, public-interest data governance, sovereign compute, and contestable digital markets. It concludes that middle powers must develop the state as a platform: a provider of trusted digital foundations that can sustain innovation, democratic accountability, and strategic autonomy in an increasingly platformized world economy.

Skill extraction is at the core of algorithmic hiring. It is based on identifying terms commonly found in both targets (i.e., resumes and job offers), aiming at identifying a “match” or correspondence between both. This article focuses on skill extraction from resumes, as opposed to job offers, and considers this task both from the human resource management (HRM) and artificial intelligence (AI) points of view. We discuss challenges identified by both fields and explain how collaboration is instrumental for a successful digital transformation of HRM. We argue that annotation efforts are an ideal example of where collaboration between both fields is needed and present an annotation effort on 46 resumes with 41 trained annotators, resulting in a total of 116 annotations. We analyze the skills extracted by multiple different systems and compare those to the skills selected by the annotators, and find that the skills extracted differ a lot in terms of length and semantic content. The skills extracted using conversational large language models (LLMs) tend to be very long and detailed; other systems are concise, whereas humans are in the middle. In terms of semantic similarity, conversational LLMs are closer to human outputs than other systems. Our analysis proposes a different perspective to understand the well-studied, but still unsolved, skill extraction task. Finally, we provide policy recommendations for skill extraction aligned with both HRM and computational perspectives.

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.

Although a large body of policy and practice exists on the data governance of medical applications, this paper is the first to provide a systematic overview of human–AI interactions and data risks/governance in consumer-oriented health and wellness applications, which are a growing area of personal health information management. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, 116 relevant articles, 9 representative cases, and 81 policy documents from official government websites were collected and analysed using thematic analysis. Case studies were used to validate the thematic findings and identify practical response measures to governance risks. The current state of human–AI interaction is discussed in four dimensions in the literature: application scenarios, service contents, AI technologies applied, and interfaces. The interaction data types generated are related to physical indicators, lifestyle habits, personal identity and social relations, digital behaviour, health advice, and others. The data governance practices and risks comprise six categories: governance subjects, policies, standards and principles, technologies and facilities, lifecycle measures, and data rights protection and risk management. Among the most critical risks are data privacy breaches, unclear data ownership, inconsistent standards, and poor data quality. The findings highlight that current responses—primarily focused on compliance, privacy protection, and technical optimisation—remain fragmented and inadequate for addressing the critical risks identified. This study provides practical insights for policymakers, developers, and platform providers seeking to strengthen collaborative, adaptive, and trust-based governance in AI-enabled health applications.

This article introduces the global Women’s Safety Index (WSI), outlining its rationale, purpose, and potential applications. The Index consist of three dimensions: Equity, Protection, and Resources, identified as foundational to women’s safety. Key indicators within each dimension are selected based on theoretical relevance and empirical evidence. We detail the statistical methodology and framework used to construct the Index and present validation analyses demonstrating its ability to capture changes in women’s safety, particularly in response to external disruptions. The WSI is available on an interactive digital platform, enabling users to explore, visualize, and compare women’s safety data across regions and over time.

Gauging the extent of public acceptability of reforms is an important concern for policymakers. Timely insights into public perceptions can illuminate how reforms are received and how attitudes evolve over time. In this study, we build on the OECD’s Public Acceptability Tool, a framework encompassing four key dimensions of reform acceptability—Economic, Fairness, Behavioural, and Process—to evaluate the public acceptability of policy reforms. We take the 2023 French pension reform as a relevant case study, using online media articles and parliamentary speeches as indicators of discourse surrounding the reform. Using word embeddings, we classify these texts according to the four dimensions and apply matrix factorisation topic algorithms to uncover the latent themes within each. Our analysis shows that the Process dimension dominated media coverage during the discussion and legislative phases of the reform, consistent with previous literature on pension reforms. In contrast, no particular dimension was predominant in parliamentary speeches, suggesting a mismatch between policy and public debates. Finally, we identify the main topics driving public discussion within each dimension, highlighting notable differences between media narratives and parliamentary discourse that offer further insight into the dynamics of public acceptability.

Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning (ML) models, which raise issues of transparency and fairness. The present paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of predictive performance, interpretability, and fairness. It is shown that explainable boosting machines, a recent interpretable model, perform nearly as well as the best black-box models. It is also shown how model sparsity, feature smoothing, and fairness mitigation can enhance transparency and fairness with only minor losses in performance. These findings suggest that interpretable profiling provides an accountable and trustworthy alternative to black-box models without compromising performance.

The growing availability of information sources has offered central banks new opportunities to enhance their statistical, analytical, and policy functions. By linking—or integrating—various data sets, they have been able to produce more granular, timely, and diverse statistics in a cost-efficient way. These advancements have also enabled a better use of information available in society, such as administrative records, to improve statistical agility in responding to user needs. Yet integrating alternative data—often generated as a by-product of other processes—also raises challenges, including concerns over accuracy, representativeness, and reliability. This paper aims to review systematically the opportunities and limitations of data integration in central banks, taking stock of their experience thus far. Results underscore the need for strengthening the global statistical infrastructure through adequate data governance, management, and public resources.

The rapid proliferation of AI systems has outpaced regulatory and insurance frameworks, leaving risks from unpredictable rogue AI behaviors unaddressed. While academic debates prioritize existential threats, this article shifts focus to governing present-day AI through AI Risk Bonds: market-driven instruments inspired by catastrophe bonds. These bonds securitize AI-related liabilities, using investor scrutiny to price risks based on a system’s expected impact and behavioral predictability. By dynamically adjusting bond yields, higher risks escalate capital costs for developers, incentivizing proactive risk mitigation. The mechanism addresses regulatory blind spots via market oversight, disperses liability through capital markets, and reduces moral hazard by linking financing to risk profiles. Complementing initiatives like the EU AI Act, this framework balances innovation with precaution, tethering profitability to risk minimization for responsible AI development.

Gender data gaps in the United Kingdom are political choices that reflect deeper assumptions about whose labour, lives, and experiences are deemed worth measuring. This paper examines the strengths and limitations of the UK census for bridging these gaps, drawing on feminist participatory action research with 170 participants across 12 workshops in England, Wales, Scotland, and Northern Ireland. Participants comprised representatives from local government, women’s and non-profit organisations, academia, and unaffiliated individuals with varying statistical skills. Through co-produced inquiry, participants identified critical gender data gaps and questioned the political and epistemological assumptions embedded in census design. We find that while the UK census provide comprehensive disaggregated multivariate data at the local level, they systematically omit critical dimensions of gendered life, including income, unpaid childcare, and occupational segregation. Drawing on Fricker’s (2007) concept of epistemic injustice, we argue that these omissions are not incidental but structural: the census lacks the conceptual framework to recognise them as dimensions worth counting. Realising the census’s potential for feminist analysis, therefore, requires deliberate reshaping of data systems and practices.Crucially, we demonstrate that involving those affected by gender data gaps as co-analysts, rather than consultation subjects, surfaces blind spots and analytical priorities that academic or statistical institutions are unlikely to identify from within, making the case for embedding participatory mechanisms in how official statistics are designed, produced, and used. The paper concludes with four user-informed recommendations for census reform and the wider reimagining of UK gender data systems.

Blockchain technology has gained immense popularity in enhancing the security and privacy of Information Systems (IS), reflected in exponential increases in published research articles. The rapid proliferation of published research presents significant challenges for manual analysis and synthesis due to the vast volume of information. To this end, we adopted the Computational Literature Review (CLR) and Latent Dirichlet Allocation (LDA) topic modeling techniques to analyze the impact of the pertinent literature. This study is among the first studies in the IS field to apply the CLR technique in the Legal-Tech context, focusing on security, privacy, and governance in blockchain systems. Using CLR and LDA topic modeling, we identified 10 topics related to security and privacy, each accompanied by an in-depth description aligned with relevant policies and regulations. Our findings identify both the strengths and current gaps in the literature, providing valuable insights into ongoing scholarly discourse. Through our analysis, we outline future research directions that align with the evolving dynamics of blockchain technology, data management, and policy implications, aiming to guide further academic and practical advancements in the IS field.

This study focuses on a multidimensional evaluation of China’s artificial intelligence (AI) governance policies by employing the Policy Modeling Consistency (PMC) index model to systematically analyze the performance and coherence of these policies in terms of goal setting, technological attributes, risk governance, and socio-cultural institutional embedding. The study conceptualizes AI as a novel form of cultural and social technology rather than merely an intelligent agent. This perspective highlights AI’s role in reorganizing and restructuring information flow, cultural expression, and institutional operations. By constructing a PMC evaluation framework that integrates technological, risk, and socio-cultural dimensions, this research reveals both strengths and weaknesses in the current policy system, particularly significant gaps in embedding socio-cultural institutions. Combining theoretical and empirical analyses, the PMC model offers an effective framework for understanding the institutional dynamics of AI governance in China. The findings enrich theoretical perspectives on AI governance and provide empirical support for policy formulation.

Indonesia’s Tax Court faces a rapidly growing caseload and limited judicial capacity, leading to prolonged dispute resolution and eroding public trust. Manual review processes create inconsistencies that undermine legal certainty, particularly within Indonesia’s civil law system, where jurisprudence is nonbinding and a judge’s personal knowledge may serve as admissible evidence. To address these challenges, we introduce the AI-driven Decision and Integrity for Tax Court Law (ADIL) pipeline, which automates the analysis of 4377 Indonesian Tax Court decisions from 2008 to 2023. ADIL applies advanced natural language processing and legal informatics to extract structured insights from unstructured judicial texts through a trilayered architecture: (i) rule-based pattern matching for high-precision extraction, (ii) transformer-based named-entity recognition for robust entity detection, and (iii) local large-language-model prompting for semantic summarisation and classification. Using exclusively pre-decision metadata, 49 structured metadata fields, including dispute number, tax year, judge identity, and verdict, were used to train a model that resulted in several machine-learning models to predict case outcomes and resolution duration. ADIL achieved 97.5% precision and strong macro-F1 performance in outcome prediction, and an R2 of 92.9% (RMSE = 63.8%) for duration estimation, equivalent to 7–8 months of predictive accuracy. Notably, incorporating encoded judge identity improved accuracy by 11–16%, revealing systematic variation in judicial behaviour. Feature-importance analysis suggests that ADIL can substantially enhance efficiency, consistency, and transparency in tax adjudication. ADIL offers a data-driven decision support framework towards a more agile and accountable judicial system in Indonesia’s legal and institutional context.

Rapid developments in Artificial Intelligence (AI) tools and their ever-expanding use in societal contexts raises questions regarding their design, social acceptance, and regulation. We develop a randomized vignette experiment, detailing six contemporary high-stakes contexts employing algorithmic tools and uncover distinct attitudes toward the source of the AI provisioning (public vs private sector). Participants are drawn from technology and law schools in India, representing key future stakeholders in the AI policy ecosystem in the country. Our main result indicates an overall preference for public sector provisioning of AI tools, with important implications on the social acceptability of public sector expansion of AI systems. Although the Global South has witnessed widespread expansion of AI tools with limited scrutiny, the nascent scholarship on AI attitudes disproportionately emanates from the Global North. Our study fills this gap, as well as the gap in empirical evidence on private vs public provisioning of AI tools in social contexts.

This article explores the integration of large language models (LLMs) and AI research agents into global benchmarking frameworks, with a focus on data for the public good. Against a backdrop of shrinking funding and rising demand for scalable and reproducible assessments, we ask whether AI can assume core roles in indicator development, evidence discovery, and policy evaluation without compromising contextual nuance or democratic legitimacy. Building on pilot experiments conducted within the Global Data Barometer (GDB), we employed a phased, adaptive methodology that tested workflow-based platforms and deep research agents across tasks ranging from legal interpretation to multisource policy analysis. The preliminary findings suggest that while AI systems show strong potential for automating structured assessments, they falter on complex, fragmented, or normatively loaded indicators, raising concerns about opacity, overinterpretation, and inclusivity. To navigate these tensions, we propose a hybrid human-AI architecture that combines standardized workflows, adaptive agent capabilities, and critical human oversight. Central to this model is the concept of a dynamic evidence infrastructure, designed to embed participatory validation and enhance transparency. By reframing automation as augmentation, the study contributes both an empirical, domain-specific assessment of the opportunities and limits of AI-assisted benchmarking and a theoretical framework for sustainable, context-aware evaluation in the age of AI. We argue that the success of AI-assisted benchmarking should be measured not only in efficiency gains but also in its ability to strengthen legitimacy, accountability, and inclusiveness in data ecosystems worldwide.

As large language models (LLMs) increasingly shape decision-making, public discourse, education, healthcare, and governance, a critical question emerges: Whose values are these systems truly reflecting? While today’s generative AI systems demonstrate extraordinary capabilities, they also inherit hidden biases, ethical blind spots, and implicit assumptions embedded within their training data. Existing alignment approaches often remain opaque, resource-intensive, or insufficiently adaptive to diverse societal expectations.

This paper introduces a novel value-driven LLM framework designed to systematically uncover, quantify, and realign the implicit values embedded within LLMs toward socially desirable outcomes. Built upon the AI for Social Good (AIfSG) framework, our proposed methodology operationalizes ethical alignment across six critical domains: reasoning and interpretability, bias removal, transparency and accountability, security and privacy, moral and ethical observations, and public understanding. Using advanced embedding techniques, cosine-based value-difference metrics, and topic-weighted iterative fine-tuning, the framework transforms ethical alignment from an abstract aspiration into a measurable and actionable computational process.

To demonstrate adaptability across moral and regulatory paradigms, the framework evaluates two distinct reference value systems: the Ten Commandments and the General Data Protection Regulation (GDPR). Experimental results using open-source LLMs, including Llama 3.2 and Gemma 2, reveal substantial reductions in value misalignment, ranging from approximately 25% to 70% across ethical domains, while preserving model flexibility and scalability. Visualizations in value-embedding space further confirm significant convergence between original model outputs and socially aligned reference values after iterative realignment.

Beyond technical innovation, this work positions value alignment as a foundational challenge for the future governance of generative AI. The proposed framework functions both as a diagnostic instrument for identifying ethical gaps and as an intervention mechanism for adaptive value realignment, offering policymakers, developers, and institutions a scalable pathway toward transparent, accountable, and socially responsible AI systems. By bridging computational methods with moral, legal, and societal principles, this research advances a new paradigm for embedding human-centered values directly into the next generation of intelligent systems.

Democratic resilience is as much about the narratives of our nation we affirm, as the institutions that enshrine our values and laws, a fact re-affirmed by scholarship across many branches of social science in recent decades. For policymakers and quantitative social scientists, analysing or tracking public discourse through the lens of narrative and framing has historically involved the annotation of texts by hand, placing severe limitations on the scale and modality of discourse under inquiry. Yet, a revolution is at hand—a transformer revolution: first arising in computer science, and now enabling a new kind of automated narrative analysis at scale, transformers are opening up new horizons for the tracking of public narratives of democratic resilience. Here, we: formulate a conceptual framework linking computational language methods to democratic resilience analysis; introduce transformer-based artificial intelligence (AI) methods as a third wave in natural language processing technology; and demonstrate two practical applications of transformer methods to democratic narrative analysis. Finally, we conclude by contributing data and research recommendations which flow naturally from the opportunities unlocked by transformer methods for public stakeholders who wish to see these opportunities realised. Together, we suggest that, perhaps for the first time, the “holy grail” of the quantitative social scientist is near: the ability to identify, accurately, and efficiently, nuanced narratives in text, at scale.