While governments have long discussed the promise of delegating important decisions to machines, actual use often lags. Consequently, we know little about the variation in the deployment of such delegations in large numbers of similar governmental organizations. Using data from crime laboratories in the United States, we examine the uneven distribution over time of a specific, well-known expert system for ballistics imaging for a large sample of local and regional public agencies; an expert system is an inference engine joined with a knowledge base. Our statistical model is informed by the push-pull-capability theory of innovation in the public sector. We test hypotheses about the probability of deployment and provide evidence that the use of this expert system varies with the pull of agency task environments and the enabling support of organizational resources—and that the impacts of those factors have changed over time. Within this context, we also present evidence that general knowledge of the use of expert systems has supported the use of this specific expert system in many agencies. This empirical case and this theory of innovation provide broad evidence about the historical utilization of expert systems as algorithms in public sector applications.

The “la Caixa” Foundation has been experimenting with artificial intelligence (AI)-assisted decision-making geared toward alleviating the administrative burden associated with the evaluation pipeline of its flagship funding program, piloting an algorithm to detect immature project proposals before they reach the peer review stage, and suggest their removal from the selection process to a human overseer. In this article, we explore existing uses of AI by publishers and research funding organizations to automate their selection pipelines, in addition to analyzing the conditions under which the focal case corresponds to a responsible use of AI and the extent to which these conditions are met by the current implementation, highlighting challenges and areas of improvement.

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.

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.

While the number of international students attending UK universities has been increasing in recent years, the 2021/22 and 2022/23 academic years saw a decline in applications from EU-domiciled students. However, the extent and varying impact of this decline remain to be estimated and disentangled from the impacts of the COVID-19 pandemic. Using difference-in-differences (DID) in a hierarchical regression framework and Universities and Colleges Admissions Service (UCAS) data, we aim to quantify the decline in the number of student applications post-Brexit. We find evidence of an overall decline of 65% in the 2021 academic year in successful applications from EU students as a result of Brexit. This decline is more pronounced for non-Russell Group institutions, as well as for Health and Life Sciences and Arts and Languages. Furthermore, we explore the spatial heterogeneity of the impact of Brexit across EU countries of origin, observing the greatest effects for Poland and Germany, though this varies depending on institution type and subject. We also show that higher rates of COVID-19 stringency in the country of origin led to greater applications for UK higher education institutions. Our results are important for government and institutional policymakers seeking to understand where losses occur and how international students respond to external shocks and policy changes. Our study quantifies the distinct impacts of Brexit and COVID-19 and offers valuable insights to guide strategic interventions to sustain the UK’s attractiveness as a destination for international students.

This paper aims at exploring the dynamic interplay between advanced technological developments in AI and Big Data and the sustained relevance of theoretical frameworks in scientific inquiry. It questions whether the abundance of data in the AI era reduces the necessity for theory or, conversely, enhances its importance. Arguing for a synergistic approach, the paper emphasizes the need for integrating computational capabilities with theoretical insight to uncover deeper truths within extensive datasets. The discussion extends into computational social science, where elements from sociology, psychology, and economics converge. The application of these interdisciplinary theories in the context of AI is critically examined, highlighting the need for methodological diversity and addressing the ethical implications of AI-driven research. The paper concludes by identifying future trends and challenges in AI and computational social science, offering a call to action for the scientific community, policymakers, and society. Being positioned at the intersection of AI, data science, and social theory, this paper illuminates the complexities of our digital era and inspires a re-evaluation of the methodologies and ethics guiding our pursuit of knowledge.

Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, that is, there is no globally interpretable way to understand how a model makes estimates. This is a clear problem for governments who want to evaluate policy as it is difficult to understand whether such models are functioning in ways that are fair, based on the correct interpretation of evidence and transparent enough to allow for accountability if things go wrong. However, there has been little discussion of transparency problems in the causal machine learning literature and how these might be overcome. This article explores why transparency issues are a problem for causal machine learning in public policy evaluation applications and considers ways these problems might be addressed through explainable AI tools and by simplifying models in line with interpretable AI principles. It then applies these ideas to a case study using a causal forest model to estimate conditional average treatment effects for returns on education study. It shows that existing tools for understanding black-box predictive models are not as well suited to causal machine learning and that simplifying the model to make it interpretable leads to an unacceptable increase in error (in this application). It concludes that new tools are needed to properly understand causal machine learning models and the algorithms that fit them.

Urban logistics has emerged as a priority to improve goods distribution and mobility within urban centers worldwide. Brazil presents a unique set of challenges in this regard due to issues such as excessive reliance on road transportation, lack of regulations, inadequate infrastructure, cargo theft, and the intricate interplay of cargo transportation with urban traffic. These challenges collectively exert a substantial influence on the economic, urban, and environmental performance of cities. This article introduces a novel approach aimed at assessing and benchmarking urban logistics performance between Brazilian cities with potential applicability to other contexts. The methodology was based on data envelopment analysis to evaluate efficiency based on key indicators, including GDP Gross Domestic Product, population size, commercial establishments, urban area coverage, cargo fleet size, and travel time. By applying this methodology to 12 Brazilian cities, the study improves the understanding of their relative efficiency levels concerning urban logistics and provides key insights for policymaking. The results also show the relevance of the proposed methodology and contribute to provide a perspective of different administrative and logistical facets through the lens of macroeconomic indicators, contributing to a holistic understanding of urban logistics dynamics.

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.

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. Machine learning has previously been used to reliably “screen” articles for review – that is, identify relevant articles based on reviewers’ inclusion criteria. The application of machine learning techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We, therefore, set out to develop a series of tools that would assist in the profiling and analysis of 1952 publications on the theme of “outcomes-based contracting.” Tools were developed for the following tasks: assigning publications into “policy area” categories; identifying and extracting key information for evidence mapping, such as organizations, laws, and geographical information; connecting the evidence base to an existing dataset on the same topic; and identifying subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of machine learning techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Beyond this, our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While machine learning techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analyzing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.

Carbon neutrality cannot be achieved without different economic sectors, individuals and households, and the government making serious efforts. Green finance in different forms including environmental, social and governance investment and carbon emissions trading are used to measure the reduction in carbon emissions and place a monetary value on them. However, because of inconsistencies or even manipulation in the monitoring/measurement, reporting and verification (MRV) of air quality and carbon emissions data, the effectiveness of green finance has been largely compromised. Environmental MRV is a technology-based engineering task, which is also heavily impacted by institutional design and professionalism. This commentary will draw upon principal–agent theory and the practical arrangements of environmental MRV to discuss why professionalism is badly needed and how to bridge the missing link for achieving carbon neutrality and sustainability transitions.

In the era of the Industrial Revolution 4.0 (IR 4.0), the adequacy of training models for industrial needs is being challenged. Africa is a skills hub, threatened by unemployment among young people, especially graduates, competition, and the sustainability of industrial fabrics. By carrying out a systematic literature review, this article aims to highlight the aspects and outcomes of the educational revolution that must accompany IR 4.0. The results show that IR 4.0 offers new careers, and that training is a key barrier to the successful digital transformation of the industry. University 4.0 is the conversion needed to overcome this barrier. This article explains this new academic model generating skills, which refers to the ability to perform activities effectively with high technical, digital, and flexible management capacities. Faced with the low adoption of IR 4.0, and the lack of a systematic literature review, this article offers a significant platform for the research community, both academic and industrial.

A number of data governance policies have recently been introduced or revised by the Indian Government with the stated goal of unlocking the developmental and economic potential of data. The policies seek to implement standardized frameworks for public data management and establish platforms for data exchange. However, India has a longstanding history of record-keeping and information transparency practices, which are crucial in the context of data management. These connections have not been explicitly addressed in recent policies like the Draft National Data Governance Framework, 2022. To understand if record management has a role to play in modern public data governance, we analyze the key new data governance framework and the associated Indian Urban Data Exchange platform as a case study. The study examines the exchange where public records serve as a potential source of data. It evaluates the coverage and the actors involved in the creation of this data to understand the impact of records management on government departments’ ability to publish datasets. We conclude that while India recognizes the importance of data as a public good, it needs to integrate digital records management practices more effectively into its policies to ensure accurate, up-to-date, and accessible data for public benefit.

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 momentum surrounding the use of data for the public good has grown over the past few years, resulting in several initiatives, and rising interest from public bodies, intergovernmental organizations, and private organizations. The potential benefits of data collaboratives (DCs) have been proved in several contexts, including health, migration, pandemics, and public transport. However, these cross-sectoral partnerships have frequently not progressed beyond the pilot level, a condition hindering their ability to generate long-term societal benefits and scale their impact. Governance models play an important role in ensuring DCs’ stability over time; however, existing models do not address this issue. Our research investigates DCs’ governance settings to determine governance dimensions’ design settings enhancing DCs’ long-term stability. The research identifies through the literature on collaborative governance and DCs seven key governance dimensions for the long-term stability of DCs. Then, through the analysis of 16 heterogeneous case studies, it outlines the optimal design configurations for each dimension. Findings make a significant contribution to academic discourse by shedding light on the governance aspects that bolster the long-term stability of DCs. Additionally, this research offers practical insights and evidence-based guidelines for practitioners, aiding in the creation and maintenance of enduring DCs.

This article proposes five ideas that the design of data governance policies for the trustworthy use of artificial intelligence (AI) in Africa should consider. The first is for African states to assess their domestic strategic priorities, strengths, and weaknesses. The second is a human-centric approach to data governance, which involves data processing practices that protect the security of personal data and the privacy of data subjects; ensure that personal data are processed in a fair, lawful, and accountable manner; minimize the harmful effect of personal data misuse or abuse on data subjects and other victims; and promote a beneficial, trusted use of personal data. The third is for the data policy to be in alignment with supranational rights-respecting AI standards like the African Charter on Human and Peoples Rights, the AU Convention on Cybersecurity, and Personal Data Protection. The fourth is for states to be critical about the extent to which AI systems can be relied on in certain public sectors or departments. The fifth and final proposition is for the need to prioritize the use of representative and interoperable data and ensure a transparent procurement process for AI systems from abroad where no local options exist.

In the burgeoning landscape of African smart cities, education stands as a cornerstone for sustainable development and unlocking future potential. Accurate student performance prediction holds immense social importance, enabling early intervention, improved learning outcomes, and equitable access to quality education, aligning with sustainable development goals. Traditional models often falter in Africa due to imbalanced datasets and irrelevant features. This research leverages machine learning in Nigerian classrooms to predict underperforming students. Techniques like synthetic minority oversampling, edited nearest neighbors, and the Boruta algorithm for feature selection, alongside genetic algorithms for efficiency, enhance model performance. The ensemble models achieve AUCs of 90–99.7%, effectively separating low-performing and high-performing students. Implemented via Streamlit and Heroku, these models support real-time, data-driven decisions, enhancing early intervention, personalized learning, and informing policy and public service design. This ensures equitable education and a brighter future across Africa. By leveraging ML, this research empowers universities to support struggling students, optimize educational costs, and promote inclusive development, fostering data-driven decision-making and resource allocation optimization. Ultimately, this research paves the way for a future where data empowers education within African smart cities, unlocking the full potential of data-driven solutions and ensuring equitable educational opportunities across the continent.

Studies on court administration in India have so far focused their attention largely on caseload management and judge strength of the higher judiciary. In-depth investigations of the performance of India’s lower courts, the primary loci of a citizen’s contact with the judiciary, are rarer, largely due to the lack of available data at scale. We conduct a quantitative analysis of a large dataset of more than 1700 Indian district courts between 2010 and 2018, to assess court performance through the measure of timeliness of case disposal. We use median days to decision—the median number of days it takes for a district court in India to decide a case. We aim to understand the impact of well-established factors—working strength and tenure of judges, case administration, age distribution of cases, and category or case type—against district courts’ performance. We find that court type and nature of cases are important predictors of a district court’s performance, and that the total number of judge working days and average bench strength are not good indicators of courts’ performance—the workload per judge being actually lower in low-performance district courts, compared to high-performing courts. Our study also reveals the strengths and weaknesses of the available judicial data platforms and points toward reforms in judicial administration to address these concerns.

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.