Donor organizations and multilaterals require ways to measure progress toward the goals of creating an open internet, and condition assistance on recipient governments maintaining access to information online. Because the internet is increasingly becoming a leading tool for exchanging information, authoritarian governments around the world often seek methods to restrict citizens’ access. Two of the most common methods for restricting the internet are shutting down internet access entirely and filtering specific content. We conduct a systematic literature review of articles on the measurement of internet censorship and find that little work has been done comparing the tradeoffs of using different methods to measure censorship on a global scale. We compare the tradeoffs between measuring these phenomena using expert analysis (as measured by Freedom House and V-Dem) and remote measurement with manual oversight (as measured by Access Now and the OpenNet Initiative [ONI]) for donor organizations that want to incentivize and measure good internet governance. We find that remote measurement with manual oversight is less likely to include false positives, and therefore may be more preferable for donor organizations that value verifiability. We also find that expert analysis is less likely to include false negatives, particularly for very repressive regimes in the Middle East and Central Asia and therefore these data may be preferable for advocacy organizations that want to ensure very repressive regimes are not able to avoid accountability, or organizations working primarily in these areas.

Public agencies routinely collect administrative data that, when shared and integrated, can form a rich picture of the health and well-being of the communities they serve. One major challenge is that these datasets are often siloed within individual agencies or programs and using them effectively presents legal, technical, and cultural obstacles. This article describes work led by the North Carolina Department of Health and Human Services (NCDHHS) with support from university-based researchers to establish enterprise-wide data governance and a legal framework for routine data sharing, toward the goal of increased capacity for integrated data analysis, improved policy and practice, and better health outcomes for North Carolinians. We relied on participatory action research (PAR) methods and Deliberative Dialogue to engage a diverse range of stakeholders in the co-creation of a data governance process and legal framework for routine data sharing in NCDHHS. Four key actions were taken as a result of the participatory research process: NCDHHS developed a data strategy road map, created a data sharing guidebook to operationalize legal and ethical review of requests, staffed the Data Office, and implemented a legal framework. In addition to describing how these ongoing streams of work support data use across a large state health and human services agency, we provide three use cases demonstrating the impact of this work. This research presents a successful, actionable, and replicable framework for developing and implementing processes to support intradepartmental data access, integration, and use.

With the increased availability of data and the capacity to make sense of these data, computational approaches to analyze, model and simulate public policy evolved toward viable instruments to deliberate, plan, and evaluate them in different areas of application. Such examples include infrastructure, mobility, monetary, or austerity policies, policies on different aspects of societies (health, pandemic, skills, inclusion, etc.). Technological advances along with the evolution of theoretical models and frameworks open valuable opportunities, while at the same time, posing new challenges. The paper investigates the current state of research in the domain and aims at identifying the most pressing areas for future research. This is done through both literature research of policy modeling and the analysis of research and innovation projects that either focus on policy modeling or involve it as a significant component of the research design. In the paper, 16 recent projects involving the keyword policy modeling were analyzed. The majority of projects concern the application of policy modeling to a specific domain or area of interest, while several projects tackled the cross-cutting topics (risk and crisis management). The detailed analysis of the projects led to topics of future research in the domain of policy modeling. Most prominent future research topics in policy modeling include stakeholder involvement approaches, applicability of research results, handling complexity of models, integration of models from different modeling and simulation paradigms and approaches, visualization of simulation results, real-time data processing, and scalability. These aspects require further research to appropriately contribute to further advance the field.

Rough sleeping is a chronic experience faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link (HL), a UK-based charity, in developing a data-driven approach to better connect people sleeping rough on the streets with outreach service providers. HL's platform has grown exponentially in recent years, leading to thousands of alerts per day during extreme weather events; this overwhelms the volunteer-based system they currently rely upon for the processing of alerts. In order to solve this problem, we propose a human-centered machine learning system to augment the volunteers' efforts by prioritizing alerts based on the likelihood of making a successful connection with a rough sleeper. This addresses capacity and resource limitations whilst allowing HL to quickly, effectively, and equitably process all of the alerts that they receive. Initial evaluation using historical data shows that our approach increases the rate at which rough sleepers are found following a referral by at least 15% based on labeled data, implying a greater overall increase when the alerts with unknown outcomes are considered, and suggesting the benefit in a trial taking place over a longer period to assess the models in practice. The discussion and modeling process is done with careful considerations of ethics, transparency, and explainability due to the sensitive nature of the data involved and the vulnerability of the people that are affected.

The coronavirus disease-2019 (COVID-19) pandemic has led to the irrational use of drugs in the absence of clinical management guidelines. Access to individual participant data (IPD) from clinical trials aids the evidence synthesis approaches. We undertook a rapid review to infer IPD sharing intentions based on data availability statements by the principal investigators (PIs) of drug and vaccine trials in the context of COVID-19.

Searches were conducted on PubMed (NCBI). We considered randomized controlled trial (RCT) publications from January 1, 2020, to October 31, 2021. IPD sharing intentions were inferred based on the data availability statements in the full-text manuscript publications. We included 180 articles. Of these, 81.7% (147/180) of the publications have arrived from the findings of the RCTs alone, 12.8% (23/180) of the publications were protocol publications alone, and 5.6% (10/180) of the RCTs had both published protocol and publication from the trial findings. We have reported IPD sharing intentions separately in RCT protocol publications (n = 23 + 10) and publications from RCT findings (n = 147 + 10). Among RCT protocol publications, one-third (11/33) of the PIs intended to share IPD. In fact, over half of the PIs (52.2%, 82/157) in their published RCT findings intended to share IPD. However, information to share about IPD was missing for 57.6% (19/33) of RCT protocols and 38.2% (60/157) of published RCT findings.

Stakeholders must work together to ensure that overarching factors, such as legislation that governs clinical trial practices, are streamlined to bolster IPD sharing mechanisms.

The collection of data, its analysis, and the publication of insights from data promise a range of benefits, but can carry risks for individuals and organizations. This paper sets out considerations regarding the potential role for technologies in governance of data use, and some key limitations. The paper examines the potential of Privacy Enhancing Technologies (PETs) to support organizations and institutions that handle data in governing data use, and considers their role based on their current state of development and the trajectory of technological development. This involves consideration both of how these technologies can potentially enable governments and others to unlock the value of data, and also recognition of both contingent and in principle limitations on the role of PETs in ensuring well-governed use of data.

The aspirations-ability framework proposed by Carling has begun to place the question of who aspires to migrate at the center of migration research. In this article, building on key determinants assumed to impact individual migration decisions, we investigate their prediction accuracy when observed in the same dataset and in different mixed-migration contexts. In particular, we use a rigorous model selection approach and develop a machine learning algorithm to analyze two original cross-sectional face-to-face surveys conducted in Turkey and Lebanon among Syrian migrants and their respective host populations in early 2021. Studying similar nationalities in two hosting contexts with a distinct history of both immigration and emigration and large shares of assumed-to-be mobile populations, we illustrate that a) (im)mobility aspirations are hard to predict even under ‘ideal’ methodological circumstances, b) commonly referenced “migration drivers” fail to perform well in predicting migration aspirations in our study contexts, while c) aspects relating to social cohesion, political representation and hope play an important role that warrants more emphasis in future research and policymaking. Methodologically, we identify key challenges in quantitative research on predicting migration aspirations and propose a novel modeling approach to address these challenges.

While many Latin American countries have a tradition of receiving migrants, including the countries selected as case studies, there are no institutionalized mechanisms for the integration and settlement of migrants. The objective of this article is to explore how to improve migration data collection and management in a region that does not have many migration integration policies in place. I assess the state of migration data collection and management in three case studies: the city of Cucuta in Colombia, the North Huetar Region in Costa Rica, and the city of Monterrey in Mexico. The three countries publish data exclusively at the national level, rather than the local or municipal. Despite all case studies having a variety of administrative data, mainly in the form of entries and exits by nationality, these data are not enough to properly identify the sociodemographic characteristics of migrant populations in a country, and much less in specific cities. I make recommendations divided into three main themes to improve migration data in Latin America.

Ethical guidelines and policy documents destined to guide AI innovations have been heralded as the solution to guard us against harmful effects or to increase public value. However, these guidelines and policy documents face persistent challenges. While these documents are often criticized for their abstraction and disconnection from real-world contexts, it also occurs that stakeholders may influence them for political or strategic reasons. While this last issue is frequently acknowledged, there is seldom a means or a method provided to explore it. To address this gap, the paper employs a combination of social constructivism and science & technology studies perspectives, along with desk research, to investigate whether prior research has examined the influence of stakeholder interests, strategies, or agendas on guidelines and policy documents. The study contributes to the discourse on AI governance by proposing a theoretical framework and methodologies to better analyze this underexplored area, aiming to enhance comprehension of the policymaking process within the rapidly evolving AI landscape. The findings underscore the need for a critical evaluation of the methodologies found and a further exploration of their utility. In addition, the results aim to stimulate ongoing critical debates on this subject.

In 2022, the world experienced the deadliest year of armed conflict since the 1994 Rwandan genocide. Much of the intensity and frequency of recent conflicts has drawn more attention to failures in forecasting—that is, a failure to anticipate conflicts. Such capabilities have the potential to greatly reduce the time, motivation, and opportunities peacemakers have to intervene through mediation or peacekeeping operations. In recent years, the growth in the volume of open-source data coupled with the wide-scale advancements in machine learning suggests that it may be possible for computational methods to help the international community forecast intrastate conflict more accurately, and in doing so reduce the rise of conflict. In this commentary, we argue for the promise of conflict forecasting under several technical and policy conditions. From a technical perspective, the success of this work depends on improvements in the quality of conflict-related data and an increased focus on model interpretability. In terms of policy implementation, we suggest that this technology should be used primarily to aid policy analysis heuristically and help identify unexpected conflicts.

The present paper, which composes a broader research agenda developed by Data Privacy Brasil Research Association, aims to conduct a descriptive and qualitative study of the Brazilian National Identity System through a data protection and data justice perspective. For this purpose, a desk research and legislation analyses were conducted to answer the following question: is the Brazilian National Civil Identification System (Identificação Civil Nacional or ICN) framework adequate with the Brazilian data protection general legislation and its principles—especially regarding its information architecture? As a result, we found that the current information architecture of the ICN, as well as its current use, may reveal a set of concerns related to data protection and data justice.

Researchers have encountered many issues while studying rare illnesses such as lack of information, limited sample sizes, difficulty in diagnosis, and more. However, perhaps the biggest challenge is to recruit a large enough sample size for clinical studies; at the same time, obtaining chronological data for these patients is even more difficult. This has urged us to implement a decentralized crowdsourcing medical data sharing platform to obtain chronological rare data for certain diseases, providing both patients and other stakeholders an easier and more secure way of trading medical data by utilizing blockchain technology. This facilitates the obtention of the most elusive types of health data by dynamically allocating extra financial incentives depending on data scarcity. We also provide a novel framework for medical data cross-validation where the system checks the volunteer reviewer count. The review score depends on the count, and the more the reviewers, the bigger the final score. We also explain how differential privacy is used to protect the privacy of individual medical data while enabling data monetization.

Internet and Communication Technology/electrical and electronic equipment (ICT/EEE) form the bedrock of today’s knowledge economy. This increasingly interconnected web of products, processes, services, and infrastructure is often invisible to the user, as are the resource costs behind them. This ecosystem of machine-to-machine and cyber-physical-system technologies has a myriad of (in)direct impacts on the lithosphere, biosphere, atmosphere, and hydrosphere. As key determinants of tomorrow’s digital world, academic institutions are critical sites for exploring ways to mitigate and/or eliminate negative impacts. This Report is a self-deliberation provoked by the question How do we create more resilient and healthier computer science departments: living laboratories for teaching and learning about resource-constrained computing, computation, and communication? Our response for University College London (UCL) Computer Science is to reflect on how, when, and where resources—energy, (raw) materials including water, space, and time—are consumed by the building (place), its occupants (people), and their activities (pedagogy). This perspective and attendant first-of-its-kind assessment outlines a roadmap and proposes high-level principles to aid our efforts, describing challenges and difficulties hindering quantification of the Department’s resource footprint. Qualitatively, we find a need to rematerialise the ICT/EEE ecosystem: to reveal the full costs of the seemingly intangible information society by interrogating the entire life history of paraphernalia from smartphones through servers to underground/undersea cables; another approach is demonstrating the corporeality of commonplace phrases and Nature-inspired terms such as artificial intelligence, social media, Big Data, smart cities/farming, the Internet, the Cloud, and the Web. We sketch routes to realising three interlinked aims: cap annual power consumption and greenhouse gas emissions, become a zero waste institution, and rejuvenate and (re)integrate the natural and built environments.

The recent surge of data-driven methods in social policy have created new opportunities to assess existing poverty programs. The expectation is that the combination of advanced methods and more data can calculate the effectiveness of public interventions more accurately and tailor local initiatives accordingly. Specifically, nonmonetary indicators are increasingly being measured at micro levels in order to target social exclusion in combination with poverty. However, the multidimensional character of poverty, local context, and data matching pose challenges to data-driven analyses. By linking Dutch household-level data with policy-initiative-specific data at local level, we present an explorative study on the uptake of a local poverty pass. The goal is to unravel pass usage in terms of household income and location as well as the age of users. We find that income and age play a role in whether the pass is used, and usage differs per neighborhood. With this, the paper feeds into the discourse on how to operationalize and design data matching work in the multidimensional space of poverty and nonmonetary government initiatives.

The aim of the present article is to evaluate the use of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model in predicting spatially and temporally localized political violent events using the Integrated Crisis Early Warning System (ICEWS). The performance of the ARFIMA model is compared to that of a naïve model in reference to two common relevant hypotheses: the ARFIMA model would outperform a naïve model and the rate of outperformance would deteriorate the higher the level of spatial aggregation. This analytical strategy is used to predict political violent events in Afghanistan. The analysis consists of three parts. The first is a replication of Yonamine’s study for the period beginning in April 2010 and ending in March 2012. The second part compares the results to those of Yonamine. The comparison was used to assess the validity of the conclusions drawn in the original study, which was based on the Global Database of Events, Language, and Tone, for the implementation of this approach to ICEWS data. Building on the conclusions of this comparison, the third part uses Yonamine’s approach to predict violent events in Afghanistan over a significantly longer period of time (January 1995–August 2021). The conclusions provide an assessment of the utility of short-term localized forecasting.

In recent years, and with the COVID disruption, many companies have moved toward digitization, adopting digital supply chains for enhanced efficiency. This coincided with the Western Governments mandating, through modern slavery legislation, that multinational companies should mitigate human rights risks in their supply chains. In addition, the Indian government has been making major efforts to equip residents in India with digital identities; first with the Aadhaar identity system, and, on August 26, 2021, the eShram portal aimed specifically at registering informal workers recognizing them formally as part of the Indian labour force. This article shows how a full digitization of the supply chains might be problematic, and in the extreme, might threaten the livelihoods of homeworkers. For the homeworkers to survive the seemingly inevitable digitization, there is a clear need to ensure that they have a direct representation in the digital supply chains. Given the limited ability of the homeworkers to directly represent themselves, we need appropriate models of digital custodianship and policies for promoting their uptake. We discuss the shape that such solutions might take. Finally, an open acceptance by brands of homeworking as a part of their supply chains is called for, paving the way to a public acceptance of these workers’ right to a minimum/living wage. To engineer widespread acceptance is an insurmountable task. It is hoped that the eShram scheme will help to change the political balance in India as the informal workers now become “traceable.”

Innovative, responsible data use is a critical need in the global response to the coronavirus disease-2019 (COVID-19) pandemic. Yet potentially impactful data are often unavailable to those who could utilize it, particularly in data-poor settings, posing a serious barrier to effective pandemic mitigation. Data challenges, a public call-to-action for innovative data use projects, can identify and address these specific barriers. To understand gaps and progress relevant to effective data use in this context, this study thematically analyses three sets of qualitative data focused on/based in low/middle-income countries: (a) a survey of innovators responding to a data challenge, (b) a survey of organizers of data challenges, and (c) a focus group discussion with professionals using COVID-19 data for evidence-based decision-making. Data quality and accessibility and human resources/institutional capacity were frequently reported limitations to effective data use among innovators. New fit-for-purpose tools and the expansion of partnerships were the most frequently noted areas of progress. Discussion participants identified building capacity for external/national actors to understand the needs of local communities can address a lack of partnerships while de-siloing information. A synthesis of themes demonstrated that gaps, progress, and needs commonly identified by these groups are relevant beyond COVID-19, highlighting the importance of a healthy data ecosystem to address emerging threats. This is supported by data holders prioritizing the availability and accessibility of their data without causing harm; funders and policymakers committed to integrating innovations with existing physical, data, and policy infrastructure; and innovators designing sustainable, multi-use solutions based on principles of good data governance.

This article focuses on measuring the impact of artificial intelligence (AI) on the peace and security agenda, taking stock of recent initiatives and progress in this area. While there is a keen awareness of the fact that AI can be weaponized to become a tool of power politics and military competition, there is comparatively less systematic attention paid to what technology can do for peace. While it is important to address risk mitigation, equal space should be given to thinking about how to harness the peace potential of AI on a large scale. This study follows a series of publications that aim to assess the impact of technological innovation on peace, also referred to as PeaceTech, Global PeaceTech, peace innovation, or digital peacebuilding. The first section provides an overview of the debate on the impact of AI on peace and conflict. The second section examines conceptual frameworks and measures of the impact of AI on peace and conflict. The third section looks at the risks to peace and conflict posed by the use of AI and possible governance measures to mitigate them. The fourth section provides examples of AI-enabled initiatives that are having a positive impact on peace, providing a compass for public and private investment. The conclusion offers policy recommendations to advance the AI for peace agenda.

In the context of the ongoing Russian invasion and the uncertainties surrounding the potential return migration of millions of displaced Ukrainians, this study explores the future of (return) migration through an innovative and inclusive participatory foresight approach, engaging 20 displaced Ukrainians residing in Valencia, Spain, from May to December 2023. The foresight process included workshops, discussions via online messaging groups, interviews, participatory observations, and culminated in an open art exhibition. Through this process, we conducted a collective horizon scanning, identifying weak signals and emerging trends, followed by an examination of critical uncertainties, which led to the development of four distinct scenarios: Exhaustion Return, Energetic Return, Virtual Return, and Disconnection. The insights derived from this foresight exercise hold practical relevance for both Ukrainian and EU migration policymakers, emphasizing the importance of lived experiences in shaping anticipatory migration policies. This study also offers theoretical contributions by applying participatory foresight to the field of return migration, challenging established knowledge paradigms, and fostering a more inclusive and nuanced understanding of migration dynamics and their broader implications.