Data for Policy (dataforpolicy.org), a trans-disciplinary community of research and practice, has emerged around the application and evaluation of data technologies and analytics for policy and governance. Research in this area has involved cross-sector collaborations, but the areas of emphasis have previously been unclear. Within the Data for Policy framework of six focus areas, this report offers a landscape review of Focus Area 2: Technologies and Analytics. Taking stock of recent advancements and challenges can help shape research priorities for this community. We highlight four commonly used technologies for prediction and inference that leverage datasets from the digital environment: machine learning (ML) and artificial intelligence systems, the internet-of-things, digital twins, and distributed ledger systems. We review innovations in research evaluation and discuss future directions for policy decision-making.

The various global refugee and migration events of the last few years underscore the need for advancing anticipatory strategies in migration policy. The struggle to manage large inflows (or outflows) highlights the demand for proactive measures based on a sense of the future. Anticipatory methods, ranging from predictive models to foresight techniques, emerge as valuable tools for policymakers. These methods, now bolstered by advancements in technology and leveraging nontraditional data sources, can offer a pathway to develop more precise, responsive, and forward-thinking policies.

This paper seeks to map out the rapidly evolving domain of anticipatory methods in the realm of migration policy, capturing the trend toward integrating quantitative and qualitative methodologies and harnessing novel tools and data. It introduces a new taxonomy designed to organize these methods into three core categories: Experience-based, Exploration-based, and Expertise-based. This classification aims to guide policymakers in selecting the most suitable methods for specific contexts or questions, thereby enhancing migration policies.

We apply moral foundations theory (MFT) to explore how the public conceptualizes the first eight months of the conflict between Ukraine and the Russian Federation (Russia). Our analysis includes over 1.1 million English tweets related to the conflict over the first 36 weeks. We used linguistic inquiry word count (LIWC) and a moral foundations dictionary to identify tweets’ moral components (care, fairness, loyalty, authority, and sanctity) from the United States, pre- and post-Cold War NATO countries, Ukraine, and Russia. Following an initial spike at the beginning of the conflict, tweet volume declined and stabilized by week 10. The level of moral content varied significantly across the five regions and the five moral components. Tweets from the different regions included significantly different moral foundations to conceptualize the conflict. Across all regions, tweets were dominated by loyalty content, while fairness content was infrequent. Moral content over time was relatively stable, and variations were linked to reported conflict events.

Even though Sub-Saharan Africa (SSA) is lagging in digital technology adoption among the global average, there is substantial progress in terms of Information and Communication Technology (ICT) access and use, where it plays a crucial role in increasing the quality of life in the regions. However, digital gaps still exist within the continents, even though technology adoption across African nations has shown an increase in progress. This paper aims to explore factors that contribute to different adoption rates among three digital technologies in SSA, specifically mobile phones, fixed broadband, and fixed telephones. The methodology utilizes panel regression analysis to examine data sourced from the World Bank, which consists of 48 SSA countries from 2006 to 2022. The findings show a consistent growth in mobile phone subscriptions, different from fixed telephone and broadband internet that shows stagnant progress. Furthermore, infrastructure, and human capital are the most significant factors in addition to other influencing factors. The results of this study provide the African governments with insightful advice on addressing the digital divide and accelerating their digital transformation.

Data for Policy (dataforpolicy.org), a global community, focuses on policy–data interactions by exploring how data can be used for policy in an ethical, responsible, and efficient manner. Within its journal, six focus areas, including Data for Policy Area 1: Digital & Data-driven Transformations in Governance, were established to delineate the evolving research landscape from the Data for Policy Conference series. This review addresses the absence of a formal conceptualization of digital and data-driven transformations in governance within this focus area. The paper achieves this by providing a working definition, mapping current research trends, and proposing a future research agenda centered on three core transformations: (1) public participation and collective intelligence; (2) relationships and organizations; and (3) open data and government. The paper outlines research questions and connects these transformations to related areas such as artificial intelligence (AI), sustainable smart cities, digital divide, data governance, co-production, and service quality. This contribution forms the foundational development of a research agenda for academics and practitioners engaged in or impacted by digital and data-driven transformations in policy and governance.

Recent developments in national health data platforms have the potential to significantly advance medical research, improve public health outcomes, and foster public trust in data governance. Across Europe, initiatives such as the NHS Research Secure Data Environment in England and the Data Room for Health-Related Research in Switzerland are underway, reflecting examples analogous to the European Health Data Space in two non-EU nations. Policy discussions in England and Switzerland emphasize building public trust to foster participation and ensure the success of these platforms. Central to building public trust is investing efforts into developing and implementing public involvement activities. In this commentary, we refer to three national research programs, namely the UK Biobank, Genomics England, and the Swiss Health Study, which implemented effective public involvement activities and achieved high participation rates. The public involvement activities used within these programs are presented following on established guiding principles for fostering public trust in health data research. Under this lens, we provide actionable policy recommendations to inform the development of trust-building public involvement activities for national health data platforms.

What drives changes in the thematic focus of state-linked manipulated media? We study this question in relation to a long-running Iranian state-linked manipulated media campaign that was uncovered by Twitter in 2021. Using a variety of machine learning methods, we uncover and analyze how this manipulation campaign’s topical themes changed in relation to rising Covid-19 cases in Iran. By using the topics of the tweets in a novel way, we find that increases in domestic Covid-19 cases engendered a shift in Iran’s manipulated media focus away from Covid-19 themes and toward international finance- and investment-focused themes. These findings underscore (i) the potential for state-linked manipulated media campaigns to be used for diversionary purposes and (ii) the promise of machine learning methods for detecting such behaviors.

The REDATAM (retrieval of data for small areas by microcomputer) statistical package and format, developed by ECLAC, has been a critical tool for disseminating census data across Latin America since the 1990s. However, significant limitations persist, including its proprietary nature, lack of documentation, and restricted flexibility for advanced data analysis. These challenges hinder the transformation of raw census data into actionable information for policymakers, researchers, and advocacy groups. To address these issues, we developed Open REDATAM, an open-source and multiplatform tool that converts REDATAM data into widely supported CSV files and native R and Python data structures. By providing integration with R and Python, Open REDATAM empowers users to work with the tools they already know and perform data analyses without leaving their R or Python window. Our work emphasizes the need for a REDATAM official format specification to further enable informed policy debates that can improve policy processes’ implementation and feedback.

As data becomes a key component of urban governance, the night-time economy is still barely visible in datasets or in policies to improve urban life. In the last 20 years, over 50 cities worldwide appointed night mayors and governance mechanisms to tackle conflicts, foster innovation, and help the night-time economy sector grow. However, the intersection of data, digital rights, and 24-hour cities still needs more studies, examples, and policies. Here, the key argument is that the increasing importance of the urban night in academia and local governments claims for much-needed responsible data practices to support and protect nightlife ecosystems. By understanding these ecosystems and addressing data invisibilities, it is possible to develop a robust framework anchored in safeguarding human rights in the digital space and create comprehensive policies to help such ecosystems thrive. Night-time governance matters for the data policy community for three reasons. First, it brings together issues covered in different disciplines by various stakeholders. We need to build bridges between sectors to avoid siloed views of urban data governance. Second, thinking about data in cities also means considering the social, economic, and cultural impact of datafication and artificial intelligence on a 24-hour cycle. Creating a digital rights framework for the night means putting into practice principles of justice, ethics, and responsibility. Third, as Night Studies is an emerging field of research, policy and advocacy, there is an opportunity to help shape how, why, and when data about the night is collected and made available to society.

The complex socioeconomic landscape of conflict zones demands innovative approaches to assess and predict vulnerabilities for crafting and implementing effective policies by the United Nations (UN) institutions. This article presents a groundbreaking Augmented Intelligence-driven Prediction Model developed to forecast multidimensional vulnerability levels (MVLs) across Afghanistan. Leveraging a symbiotic fusion of human expertise and machine capabilities (e.g., artificial intelligence), the model demonstrates a predictive accuracy ranging between 70% and 80%. This research not only contributes to enhancing the UN Early Warning (EW) Mechanisms but also underscores the potential of augmented intelligence in addressing intricate challenges in conflict-ridden regions. This article outlines the use of augmented intelligence methodology applied to a use case to predict MVLs in Afghanistan. It discusses the key findings of the pilot project, and further proposes a holistic platform to enhance policy decisions through augmented intelligence, including an EW mechanism to significantly improve EW processes, thereby supporting decision-makers in formulating effective policies and fostering sustainable development within the UN.

Artificial intelligence (AI) requires new ways of evaluating national technology use and strategy for African nations. We conduct a survey of existing “readiness” assessments both for general digital adoption and AI policy in particular. We conclude that existing global readiness assessments do not fully capture African states’ progress in AI readiness and lay the groundwork for how assessments can be better used for the African context. We consider the extent to which these indicators map to the African context and what these indicators miss in capturing African states’ on-the-ground work in meeting AI capability. Through case studies of four African nations of diverse geographic and economic dimensions, we identify nuances missed by global assessments and offer high-level policy considerations for how states can best improve their AI readiness standards and prepare their societies to capture the benefits of AI.

Sexual and gender–based violence (SGBV) is a multifaceted, endemic, and nefarious phenomenon that remains poorly measured and understood, despite greater global awareness of the issue. While efforts to improve data collection methods have increased–including the implementation of the Demographic and Health Survey (DHS) in some countries–the lack of reliable SGBV data remains a significant challenge to developing targeted policy interventions and advocacy initiatives. Using a recent mixed–methods research project conducted by the authors in Sierra Leone as a case study, this paper discusses the current status of SGBV data, challenges faced, and potential research a pproaches.

Climate change exacerbates existing risks and vulnerabilities for people globally, and migration is a longstanding adaptation response to climate risk. The mechanisms through which climate change shapes human mobility are complex, however, and gaps in data and knowledge persist. In response to these gaps, the United Nations Development Programme’s (UNDP) Predictive Analytics, Human Mobility, and Urbanization Project employed a hybrid approach that combined predictive analytics with participatory foresight to explore climate change-related mobility in Pakistan and Viet Nam from 2020 to 2050. Focusing on Karachi and Ho Chi Minh City, the project estimated temporal and spatial mobility patterns under different climate change scenarios and evaluated the impact of such in-migration across key social, political, economic, and environmental domains. Findings indicate that net migration into these cities could significantly increase under extreme climate scenarios, highlighting both the complex spatial patterns of population change and the potential for anticipatory policies to mitigate these impacts. While extensive research exists on foresight methods and theory, process reflections are underrepresented. The innovative approach employed within this project offers valuable insights on foresight exercise design choices and their implications for effective stakeholder engagement, as well as the applicability and transferability of insights in support of policymaking. Beyond substantive findings, this paper offers a critical reflection on the methodological alignment of data-driven and participatory foresight with the aim of anticipatory policy ideation, seeking to contribute to the enhanced effectiveness of foresight practices.

This study examines Nigeria’s National Information Technology Development Agency Code of Practice for Interactive Computer Service Platforms as one of Africa’s first push towards digital and social media co-regulation. Already established as a regulatory practice in Europe, co-regulation emphasises the need to impose duties of care on platforms and hold them, instead of users, accountable for safe online experiences. It is markedly different from the prior (and existing) regulatory paradigm in Nigeria, which is based on direct user regulation. By analysing the Code of Practice, therefore, this study considers what Nigeria’s radical turn towards co-regulation means for digital policy and social media regulation in relation to standards, information-gathering, and enforcement. It further sheds light on what co-regulation entails for digital regulatory practice in the wider African context, particularly in terms of the balance of power realities between Global North platforms and Global South countries.

In this paper, we provide a systematic review of existing artificial intelligence (AI) regulations in Europe, the United States, and Canada. We build on the qualitative analysis of 129 AI regulations (enacted and not enacted) to identify patterns in regulatory strategies and in AI transparency requirements. Based on the analysis of this sample, we suggest that there are three main regulatory strategies for AI: AI-focused overhauls of existing regulation, the introduction of novel AI regulation, and the omnibus approach. We argue that although these types emerge as distinct strategies, their boundaries are porous as the AI regulation landscape is rapidly evolving. We find that across our sample, AI transparency is effectively treated as a central mechanism for meaningful mitigation of potential AI harms. We therefore focus on AI transparency mandates in our analysis and identify six AI transparency patterns: human in the loop, assessments, audits, disclosures, inventories, and red teaming. We contend that this qualitative analysis of AI regulations and AI transparency patterns provides a much needed bridge between the policy discourse on AI, which is all too often bound up in very detailed legal discussions and applied sociotechnical research on AI fairness, accountability, and transparency.

Research in decentralized computing, specifically in consensus algorithms, has focused on providing resistance to an adversary with a minority stake. This has resulted in systems that are majoritarian in the extreme, ignoring valuable lessons learned in law and politics over centuries. In this article, we first detail this phenomenon of majoritarianism and point out how minority protections in the nondigital world have been implemented. We motivate adding minority protections to collaborative systems with examples. We also show how current software deployment models exacerbate majoritarianism, highlighting the problem of monoculture in client software in particular. We conclude by giving some suggestions on how to make decentralized computing less hostile to those in the minority.

We discuss the emerging technology of digital twins (DTs) and the expected demands as they scale to represent increasingly complex, interconnected systems. Several examples are presented to illustrate core use cases, highlighting a progression to represent both natural and engineered systems. The forthcoming challenges are discussed around a hierarchy of scales, which recognises systems of increasing aggregation. Broad implications are discussed, encompassing sensing, modelling, and deployment, alongside ethical and privacy concerns. Importantly, we endorse a modular and peer-to-peer view for aggregate (interconnected) DTs. This mindset emphasises that DT complexity emerges from the framework of connections (Wagg et al. [2024, The philosophical foundations of digital twinning, Preprint]) as well as the (interpretable) units that constitute the whole.

Displacement continues to increase at a global scale and is increasingly happening in complex, multicrisis settings, leading to more complex and deeper humanitarian needs. Humanitarian needs are therefore increasingly outgrowing the available humanitarian funding. Thus, responding to vulnerabilities before disaster strikes is crucial but anticipatory action is contingent on the ability to accurately forecast what will happen in the future. Forecasting and contingency planning are not new in the humanitarian sector, where scenario-building continues to be an exercise conducted in most humanitarian operations to strategically plan for coming events. However, the accuracy of these exercises remains limited. To address this challenge and work with the objective of providing the humanitarian sector with more accurate forecasts to enhance the protection of vulnerable groups, the Danish Refugee Council has already developed several machine learning models. The Anticipatory Humanitarian Action for Displacement uses machine learning to forecast displacement in subdistricts in the Liptako-Gourma region in Sahel, covering Burkina Faso, Mali, and Niger. The model is mainly built on data related to conflict, food insecurity, vegetation health, and the prevalence of underweight to forecast displacement. In this article, we will detail how the model works, the accuracy and limitations of the model, and how we are translating the forecasts into action by using them for anticipatory action in South Sudan and Burkina Faso, including concrete examples of activities that can be implemented ahead of displacement in the place of origin, along routes and in place of destination.

This study analyzes National Cyber Security Strategies (NCSSs) of G20 countries through a novel combination of qualitative and quantitative methodologies. It focuses on delineating the shared objectives, distinct priorities, latent themes, and key priorities within the NCSSs. Latent dirichlet allocation topic modeling technique was used to identify implicit themes in the NCSSs to augment the explicitly articulated strategies. By exploring the latest versions of NCSS documents, the research uncovers a detailed panorama of multinational cybersecurity dynamics, offering insights into the complexities of shared and unique national cybersecurity challenges. Although challenged by the translation of non-English documents and the intrinsic limitations of topic modeling, the study significantly contributes to the cybersecurity policy domain, suggesting directions for future research to broaden the analytical scope and incorporate more diverse national contexts. In essence, this research underscores the indispensability of a multifaceted, analytical approach in understanding and devising NCSSs, vital for navigating the complex, and ever-changing digital threat environment.

The global number of individuals experiencing forced displacement has reached its highest level in the past decade. In this context, the provision of services for those in need requires timely and evidence-based approaches. How can mobile phone data (MPD) based analyses address the knowledge gap on mobility patterns and needs assessments in forced displacement settings? To answer this question, in this paper, we examine the capacity of MPD to function as a tool for anticipatory analysis, particularly in response to natural disasters and conflicts that lead to internal or cross-border displacement. The paper begins with a detailed review of the processes involved in acquiring, processing, and analyzing MPD in forced displacement settings. Following this, we critically assess the challenges associated with employing MPD in policy-making, with a specific focus on issues of user privacy and data ethics. The paper concludes by evaluating the potential benefits of MPD analysis for targeted and effective policy interventions and discusses future research avenues, drawing on recent studies and ongoing collaborations with mobile network operators.