The coupling of the disruptive processes of digitalization and the green transformation in a so-called “Twin Transformation” is already being considered a strategic step within the European Union and is discussed in the academic sphere. Strategically, this coupling is necessary and meaningful to realize synergies and to avoid counterproductive effects, such as rebound effects or lock-in effects, particularly given the time constraints imposed by climate change. The European data strategy not only calls for the establishment of various data spaces, such as the data space for the European Green New Deal, but also calls for the opening, integration, and utilization of European data for stakeholders from administration, business, and civil society. Considering this, it is argued that administrative informatics as a discipline could be integrated as an additional analytical perspective into the political science heuristic of the policy cycle. This integration offers substantial added value for analyzing and shaping the policy processes of the European Green transformation. Moreover, this heuristic approach enables the ex-ante prediction of changes in policymaking based on the theories, models, methods, and application areas of administrative informatics. Building on this premise, this article provides insights into the application of the proposed heuristic using the example of the European Green transformation. It analyzes the resulting implications for the analysis of policymaking considering an increasingly digitalized public administration.

Global food security worsened during the COVID-19 pandemic. In Nigeria, food security indicators increased in the first months of the pandemic and then decreased slightly but never returned to their pre-pandemic levels. We assess if savings groups provided household coping mechanisms during COVID-19 in Nigeria by combining the in-person LSMS-ISA/GHS-2018/19 with four rounds of the Nigerian Longitudinal Phone Survey collected during the first year of the pandemic. A quasi-difference-in-differences analysis setup leveraging the panel nature of the data indicates that savings group membership reduces the likelihood of skipping a meal but finds no statistically significant effect on the likelihood of running out of food or eating fewer kinds of food. Given theoretical priors and other literature positing a relationship, we also implement an OLS regression analysis controlling for baseline values finding that having at least one female household member in a savings group is associated with a 5–15% reduction in the likelihood of reporting skipping meals, running out of food, and eating fewer kinds of food. This analysis is not able to establish causality, however, and may in fact overestimate the effects. Together, the results indicate that savings group membership is positively associated with food security after COVID-19, but the causal effect is statistically significant for only one of the three food security indicators. To conclude, considering the interest in savings groups and expectations of continued food security shocks, the importance of collecting better gender-disaggregated longitudinal household data combined with experimental designs and institutional data on savings groups.

In this editorial, we draw insights from a special collection of peer-reviewed papers investigating how new data sources and technology can enhance peace. The collection examines local and global practices that strive towards positive peace through the responsible use of frontier technologies. In particular, the articles of the collection illustrate how advanced techniques—including machine learning, network analysis, specialised text classifiers, and large-scale predictive analytics—can deepen our understanding of conflict dynamics by revealing subtle interdependencies and patterns. Others assess innovative approaches reinterpreting peace as a relational phenomenon. Collectively, they assess ethical, technical, and governance challenges while advocating balanced frameworks that ensure accountability alongside innovation. The collection offers a practical roadmap for integrating technical tools into peacebuilding to foster resilient societies and non-violent conflict transformations.

This article examines the impact of generative artificial intelligence (GAI) on higher education, emphasizing its effects in the broader educational contexts. As AI continues to reshape the landscape of teaching and learning, it is imperative for higher education institutions to adapt rapidly to equip graduates for the challenges of a progressively automated global workforce. However, a critical question emerges: will GAI lead to a more inclusive future of learning, or will it deepen existing divides and create a future where educational access and success are increasingly unequal? This study employs both theoretical and empirical approaches to explore the transformative potential of GAI. Drawing upon the literature on AI and education, we establish a framework that categorizes the essential knowledge and skills needed by graduates in the GAI era. This framework includes four key capability sets: AI ethics, AI literacy (focusing on human-replacement technologies), human–AI collaboration (emphasizing human augmentation), and human-distinctive capacities (highlighting unique human intelligence). Our empirical analysis involves scrutinizing GAI policy documents and the core curricula mandated for all graduates across leading Asian universities. Contrary to expectations of a uniform AI-driven educational transformation, our findings expose significant disparities in AI readiness and implementation among these institutions. These disparities, shaped by national and institutional specifics, are likely to exacerbate existing inequalities in educational outcomes, leading to divergent futures for individuals and universities alike in the age of GAI. Thus, this article not only maps the current landscape but also forecasts the widening educational gaps that GAI might engender.

Participation is a prevalent topic in many areas, and data-driven projects are no exception. While the term generally has positive connotations, ambiguities in participatory approaches between facilitators and participants are often noted. However, how facilitators can handle these ambiguities has been less studied. In this paper, we conduct a systematic literature review of participatory data-driven projects. We analyse 27 cases regarding their openness for participation and where participation most often occurs in the data life cycle. From our analysis, we describe three typical project structures of participatory data-driven projects, combining a focus on labour and resource participation and/or rule- and decision-making participation with the general set-up of the project as participatory-informed or participatory-at-core. From these combinations, different ambiguities arise. We discuss mitigations for these ambiguities through project policies and procedures for each type of project. Mitigating and clarifying ambiguities can support a more transparent and problem-oriented application of participatory processes in data-driven projects.

We interrogate efforts to legislate artificial intelligence (AI) through Canada’s Artificial Intelligence and Data Act (AIDA) and argue it represents a series of missed opportunities that so delayed the Act that it died. We note how much of this bill was explicitly tied to economic development and implicitly tied to a narrow jurisdictional form of shared prosperity. Instead, we contend that the benefits of AI are not shared but disproportionately favour specific groups, in this case, the AI industry. This trend appears typical of many countries’ AI and data regulations, which tend to privilege the few, despite promises to favour the many. We discuss the origins of AIDA, drafted by Canada’s federal Department for Innovation Science and Economic Development (ISED). We then consider four problems: (1) AIDA relied on public trust in a digital and data economy; (2) ISED tried to both regulate and promote AI and data; (3) Public consultation was insufficient for AIDA; and (4) Workers’ rights in Canada and worldwide were excluded in AIDA. Without strong checks and balances built into regulation like AIDA, innovation will fail to deliver on its claims. We recommend the Canadian government and, by extension, other governments invest in an AI act that prioritises: (1) Accountability mechanisms and tools for the public and private sectors; (2) Robust workers’ rights in terms of data handling; and (3) Meaningful public participation in all stages of legislation. These policies are essential to countering wealth concentration in the industry, which would stifle progress and widespread economic growth.

This commentary examines the dual role of artificial intelligence (AI) in shaping electoral integrity and combating misinformation, with a focus on the 2025 Philippine elections. It investigates how AI has been weaponised to manipulate narratives and suggests strategies to counteract disinformation. Drawing on case studies from the Philippines, Taiwan, and India—regions in the Indo-Pacific with vibrant democracies, high digital engagement, and recent experiences with election-related misinformation—it highlights the risks of AI-driven content and the innovative measures used to address its spread. The commentary advocates for a balanced approach that incorporates technological solutions, regulatory frameworks, and digital literacy to safeguard democratic processes and promote informed public participation. The rise of generative AI tools has significantly amplified the risks of disinformation, such as deepfakes, and algorithmic biases. These technologies have been exploited to influence voter perceptions and undermine democratic systems, creating a pressing need for protective measures. In the Philippines, social media platforms have been used to spread revisionist narratives, while Taiwan employs AI for real-time fact-checking. India’s proactive approach, including a public misinformation tipline, showcases effective countermeasures. These examples highlight the complex challenges and opportunities presented by AI in different electoral contexts. The commentary stresses the need for regulatory frameworks designed to address AI’s dual-use nature, advocating for transparency, real-time monitoring, and collaboration between governments, civil society, and the private sector. It also explores the criteria for effective AI solutions, including scalability, adaptability, and ethical considerations, to guide future interventions. Ultimately, it underscores the importance of digital literacy and resilient information ecosystems in supporting informed democratic participation.

Africa had a busy election calendar in 2024, with at least 19 countries holding presidential or general elections. In a continent with a large youth population, a common theme across these countries is a desire for citizens to have their voices heard, and a busy election year offers an opportunity for the continent to redeem its democratic credentials and demonstrate its leaning towards strengthening free and fair elections and a more responsive and democratic governance. Given the central role that governance plays in security in Africa, the stakes from many of these elections are high, not only to achieve a democratically elected government but also to achieve stability and development. Since governance norms, insecurity, and economic buoyancy are rarely contained by borders, the conduct and outcomes from each of these elections will also have implications for neighbouring countries and the continent overall. This article considers how the results of recent elections across Africa have been challenged in courts based on mistrust in the use of technology platforms, how the deployment of emerging technology, including AI, is casting a shadow on the integrity of elections in Africa, and the policy options to address these emerging trends with a particular focus on governance of AI technologies through a human rights-based approach and equitable public procurement practices.

On both global and local levels, one can observe a trend toward the adoption of algorithmic regulation in the public sector, with the Chinese social credit system (SCS) serving as a prominent and controversial example of this phenomenon. Within the SCS framework, cities play a pivotal role in its development and implementation, both as evaluators of individuals and enterprises and as subjects of evaluation themselves. This study engages in a comparative analysis of SCS scoring mechanisms for individuals and enterprises across diverse Chinese cities while also scrutinizing the scoring system applied to cities themselves. We investigate the extent of algorithmic regulation exercised through the SCS, elucidating its operational dynamics at the city level in China and assessing its interventionism, especially concerning the involvement of algorithms. Furthermore, we discuss ethical concerns surrounding the SCS’s implementation, particularly regarding transparency and fairness. By addressing these issues, this article contributes to two research domains: algorithmic regulation and discourse surrounding the SCS, offering valuable insights into the ongoing utilization of algorithmic regulation to tackle governance and societal challenges.

Online customer feedback management (CFM) is becoming increasingly important for businesses. Providing timely and effective responses to guest reviews can be challenging, especially as the volume of reviews grows. This paper explores the response process and the potential for artificial intelligence (AI) augmentation in response formulation. We propose an orchestration concept for human–AI collaboration in co-writing within the hospitality industry, supported by a novel NLP-based solution that combines the strengths of both human and AI. Although complete automation of the response process remains out of reach, our findings offer practical implications for improving response speed and quality through human–AI collaboration. Additionally, we formulate policy recommendations for businesses and regulators in CFM. Our study provides transferable design knowledge for developing future CFM products.

This paper explores the evolution of the concept of peace in the context of a globalized and digitalized 21st century, proposing a novel vision that shifts from viewing peace as a thing or a condition, to understanding peace as dynamic and relational process that emerges through human interactions. Building on - yet also going beyond - traditional definitions of peace as something to be found through inner reflection (virtue ethics), as the product of reason, contracts and institutions (Enlightenment philosophy), and as the absence of different forms of violence (modern peace research), this paper introduces a new meso-level theory on networks, emphasizing the importance of connections, interactions and relationships in the physical and online worlds. The paper is structured around three main objectives: conceptualizing relational peace in terms of the quantity and quality of interactions, mapping these interactions into networks of peace, and examining how these networks interact with their environment, including the influence of digital transformation and artificial intelligence. By integrating insights from ethical and peace research literature, the paper makes theoretical, conceptual, and methodological contributions towards understanding peace as an emergent property of human behavior. Through this innovative approach, the paper aims to provide clarity on how peace (and violence) emerges through interactions and relations in an increasingly networked and digitalized global society, offering a foundation for future empirical research and concerted policy action in this area. It highlights the need for bridging normative and descriptive sciences to better understand and promote peace in the digital age.

Recent studies utilizing AI-driven speech-based Alzheimer’s disease (AD) detection have achieved remarkable success in detecting AD dementia through the analysis of audio and text data. However, detecting AD at an early stage of mild cognitive impairment (MCI), remains a challenging task, due to the lack of sufficient training data and imbalanced diagnostic labels. Motivated by recent advanced developments in Generative AI (GAI) and Large Language Models (LLMs), we propose an LLM-based data generation framework, leveraging prior knowledge encoded in LLMs to generate new data samples. Our novel LLM generation framework introduces two novel data generation strategies, namely, the cross-lingual and the counterfactual data generation, facilitating out-of-distribution learning over new data samples to reduce biases in MCI label prediction due to the systematic underrepresentation of MCI subjects in the AD speech dataset. The results have demonstrated that our proposed framework significantly improves MCI Detection Sensitivity and F1-score on average by a maximum of 38% and 31%, respectively. Furthermore, key speech markers in predicting MCI before and after LLM-based data generation have been identified to enhance our understanding of how the novel data generation approach contributes to the reduction of MCI label prediction biases, shedding new light on speech-based MCI detection under low data resource constraint. Our proposed methodology offers a generalized data generation framework for improving downstream prediction tasks in cases where limited and/or imbalanced data have presented significant challenges to AI-driven health decision-making. Future study can focus on incorporating more datasets and exploiting more acoustic features for speech-based MCI detection.

The EU’s Common European Data Space (CEDS) aims to create a single market for data-sharing in Europe, build trust among stakeholders, uphold European values, and benefit society. However, there is the possibility that the values of the EU and the benefits for the common good of European society may get overlooked for the economic benefits of organisations if norms and social values are not considered. We propose that the concept of “data commons” is relevant for defining openness versus enclosure of data in data spaces and is important when considering the balance and trade-off between individual (market) versus collective (societal) benefits from data-sharing within the CEDS. Commons are open-access resources governed by a group, either formally by regulation or informally by local customs. The application of the data commons to the CEDS would promote data-sharing for the “common good.” However, we propose that the data commons approach should be balanced with the market-based approach to CEDS in an inclusive hybrid data governance approach that meets material, price-driven interests, while stimulating collective learning in online networks to form social communities that offer participants a shared identity and social recognition.

In today’s world, smart algorithms—artificial intelligence (AI) and other intelligent systems—are pivotal for promoting the development agenda. They offer novel support for decision-making across policy planning domains, such as analysing poverty alleviation funds and predicting mortality rates. To comprehensively assess their efficacy and implications in policy formulation, this paper conducts a systematic review of 207 publications. The analysis underscores their integration within and across stages of the policy planning cycle: problem diagnosis and goal articulation; resource and constraint identification; design of alternative solutions; outcome projection; and evaluation. However, disparities exist in smart algorithm applications across stages, economic development levels, and Sustainable Development Goals (SDGs). While these algorithms predominantly focus on resource identification (29%) and contribute significantly to designing alternatives—such as long-term national energy policies—and projecting outcomes, including predicting multi-scenario land-use ecological security strategies, their application in evaluation remains limited (10%). Additionally, low-income nations have yet to fully harness AI’s potential, while upper-middle-income countries effectively leverage it. Notably, smart algorithm applications for SDGs also exhibit unevenness, with more emphasis on SDG 11 than on SDG 5 and SDG 17. Our study identifies literature gaps. Firstly, despite theoretical shifts, a disparity persists between physical and socioeconomic/environmental planning applications. Secondly, there is limited attention to policy-making in development initiatives, which is critical for improving lives. Future research should prioritise developing adaptive planning systems using emerging powerful algorithms to address uncertainty and complex environments. Ensuring algorithmic transparency, human-centered approaches, and responsible AI are crucial for AI accountability, trust, and credibility.

The alignment of artificial intelligence (AI) systems with societal values and the public interest is a critical challenge in the field of AI ethics and governance. Traditional approaches, such as Reinforcement Learning with Human Feedback (RLHF) and Constitutional AI, often rely on pre-defined high-level ethical principles. This article critiques these conventional alignment frameworks through the philosophical perspectives of pragmatism and public interest theory, arguing against their rigidity and disconnect with practical impacts. It proposes an alternative alignment strategy that reverses the traditional logic, focusing on empirical evidence and the real-world effects of AI systems. By emphasizing practical outcomes and continuous adaptation, this pragmatic approach aims to ensure that AI technologies are developed according to the principles that are derived from the observable impacts produced by technology applications.

The increasing popularity of large language models has not only led to widespread use but has also brought various risks, including the potential for systematically spreading fake news. Consequently, the development of classification systems such as DetectGPT has become vital. These detectors are vulnerable to evasion techniques, as demonstrated in an experimental series: Systematic changes of the generative models’ temperature proofed shallow learning—detectors to be the least reliable (Experiment 1). Fine-tuning the generative model via reinforcement learning circumvented BERT-based—detectors (Experiment 2). Finally, rephrasing led to a >90% evasion of zero-shot—detectors like DetectGPT, although texts stayed highly similar to the original (Experiment 3). A comparison with existing work highlights the better performance of the presented methods. Possible implications for society and further research are discussed.