Trustworthiness is typically regarded as a desirable feature of national identification systems (NISs); but the variegated nature of the trustor communities associated with such systems makes it difficult to see how a single system could be equally trustworthy to all actual and potential trustors. This worry is accentuated by common theoretical accounts of trustworthiness. According to such accounts, trustworthiness is relativized to particular individuals and particular areas of activity, such that one can be trustworthy with regard to some individuals in respect of certain matters, but not trustworthy with regard to all trustors in respect of every matter. The present article challenges this relativistic approach to trustworthiness by outlining a new account of trustworthiness, dubbed the expectation-oriented account. This account allows for the possibility of an absolutist (or one-place) approach to trustworthiness. Such an account, we suggest, is the approach that best supports the effort to develop NISs. To be trustworthy, we suggest, is to minimize the error associated with trustor expectations in situations of social dependency (commonly referred to as trust situations), and to be trustworthy in an absolute sense is to assign equal value to all expectation-related errors in all trust situations. In addition to outlining the features of the expectation-oriented account, we describe some of the implications of this account for the design, development, and management of trustworthy NISs.

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.

The US government invests substantial sums to control the HIV/AIDS epidemic. To monitor progress toward epidemic control, PEPFAR, or the President’s Emergency Plan for AIDS Relief, oversees a data reporting system that includes standard indicators, reporting formats, information systems, and data warehouses. These data, reported quarterly, inform understanding of the global epidemic, resource allocation, and identification of trouble spots. PEPFAR has developed tools to assess the quality of data reported. These tools made important contributions but are limited in the methods used to identify anomalous data points. The most advanced consider univariate probability distributions, whereas correlations between indicators suggest a multivariate approach is better suited. For temporal analysis, the same tool compares values to the averages of preceding periods, though does not consider underlying trends and seasonal factors. To that end, we apply two methods to identify anomalous data points among routinely collected facility-level HIV/AIDS data. One approach is Recommender Systems, an unsupervised machine learning method that captures relationships between users and items. We apply the approach in a novel way by predicting reported values, comparing predicted to reported values, and identifying the greatest deviations. For a temporal perspective, we apply time series models that are flexible to include trend and seasonality. Results of these methods were validated against manual review (95% agreement on non-anomalies, 56% agreement on anomalies for recommender systems; 96% agreement on non-anomalies, 91% agreement on anomalies for time series). This tool will apply greater methodological sophistication to monitoring data quality in an accelerated and standardized manner.

The welfare state is currently undergoing a transition toward data-driven policies, management, and execution. This has important repercussions for frontline bureaucrats in such a “digital welfare state.” So far, impact of data-driven tools on frontline bureaucrats is primarily described in terms of curtailing or enlarging their discretionary space to make decisions. It is unclear, however, how daily work practices and role identities of frontline bureaucrats change in situ and which norms they develop to work with new data tools. In this article, we present an empirical study about the impact of a data dashboard in the Work and Income department of the municipality of Rotterdam. We answer the following research question: Which role identities, work practices, and norms of appropriate behavior of frontline bureaucrats in the social domain are reshaped by the introduction of a data dashboard? We use a multiple methods design consisting of semi-structured interviews, ethnographic observations, and document analysis. Our results reveal two role identities among frontline bureaucrats: (a) the client coach, and (b) the caseload manager. We show that the implementation of the dashboard stimulates a shift from a client coach role identity toward a caseload manager role identity. This shift is contested as it leads to role identity conflicts among frontline bureaucrats with a client coach role. Furthermore, we establish that the accommodation of the institutional void in which the introduction of the dashboard takes place, is centered around three themes of contestation: (a) data quality, (b) quality of service provision, and (c) data representations.

In this article, we explore the challenges of global governance and the particular challenge presented by global data governance. We discuss a range of challenges to developing meaningful global governance institutions for regulating how companies and governments around the world manage and utilize consumer data. These challenges are compounded by their global nature and the complexities of Internet-based technologies. We argue that the following gaps exist for effective global data governance: (a) there is no overarching global framework for protecting consumer data, and it is partial and incomplete; (b) there is a lack of data protection for international data transfers, as much of the regulation that is being developed is not global in scale; and (c) new areas of data collection and use compound concerns to effective data governance in a globalized digital world. Moreover, we highlight important needs in terms of both global governance and impending challenges related to current and new uses of data. Any global governance framework should recognize the need for an iterative process where communication is ongoing between the necessary stakeholders. Agreements should incorporate common goals to maximize the potential development of global data governance norms. However, goals must remain flexible to the different data environments across nation-states while maintaining a global scope to ensure data protection. In addition, any agreement should consider the emerging challenges in this area. These challenges include new methods of data collection and use, as well as protecting individuals from manipulation and undue influence based on how their data are being used, processed, and collected.

The response to the COVID-19 pandemic has, from the outset, been characterized by a strong focus on real-time data intelligence and the use of data-driven technologies. Against this backdrop, this article investigates the impacts of the pandemic on Scottish local government’s data practices and, in turn, whether the crisis acted as a driver for digital transformation. Mobilizing the literatures on digital government transformation, and on the impacts of crises on public administrations, the article provides insights into the dynamics of digital transformation during a heightened period of acute demands on the public sector. The research evidences an intensification of public sector data use and sharing in Scottish local authorities, with focus on health-related data and the integration of existing datasets to gather local intelligence. The research reveals significant changes related to the technical and social systems of local government organizations. These include the repurposing and adoption of information systems, the acceleration of inter and intraorganizational data sharing processes, as well as changes in ways of working and in attitudes toward data sharing and collaborations. Drawing on these findings, the article highlights the importance of identifying and articulating specific data needs in relation to concrete policy questions in order to render digital transformation relevant and effective. The article also points to the need of addressing the persistent systemic challenges underlying public sector data engagement through, on one hand, sustained investment in data capabilities and infrastructures and, on the other, support for cross-organizational collaborative spaces and networks.

Today’s conflicts are becoming increasingly complex, fluid, and fragmented, often involving a host of national and international actors with multiple and often divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense of conflict dynamics, such as the range of conflict parties and the evolution of their political positions, the distinction between relevant and less relevant actors in peace-making, or the identification of key conflict issues and their interdependence. International peace efforts appear ill-equipped to successfully address these challenges. While technology is already being experimented with and used in a range of conflict related fields, such as conflict predicting or information gathering, less attention has been given to how technology can contribute to conflict mediation. This case study contributes to emerging research on the use of state-of-the-art machine learning technologies and techniques in conflict mediation processes. Using dialogue transcripts from peace negotiations in Yemen, this study shows how machine-learning can effectively support mediating teams by providing them with tools for knowledge management, extraction and conflict analysis. Apart from illustrating the potential of machine learning tools in conflict mediation, the article also emphasizes the importance of interdisciplinary and participatory, cocreation methodology for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation.

The transition to open data practices is straightforward albeit surprisingly challenging to implement largely due to cultural and policy issues. A general data sharing framework is presented along with two case studies that highlight these challenges and offer practical solutions that can be adjusted depending on the type of data collected, the country in which the study is initiated, and the prevailing research culture. Embracing the constraints imposed by data privacy considerations, especially for biomedical data, must be emphasized for data outside of the United States until data privacy law(s) are established at the Federal and/or State level.

The 2014 Research Excellence Framework (REF) assessed the quality of university research in the UK. 20% of the assessment was allocated according to peer review of the impact of research, reflecting the growing importance of impact in UK government policy. Beyond academia, impact is defined as a change or benefit to the economy, society, culture, public policy or services, health, the environment, or quality of life. Each institution submitted a set of four-page impact case studies. These are predominantly free-form descriptions and evidences of the impact of study. Numerous analyses of these case studies have been conducted, but they have utilised either qualitative methods or primary forms of text searching. These approaches have limitations, including the time required to manually analyse the data and the frequently inferior quality of the answers provided by applying computational analysis to unstructured, context-less free text data. This paper describes a new system to address these problems. At its core is a structured, queryable representation of the case study data. We describe the ontology design used to structure the information and how semantic web related technologies are used to store and query the data. Experiments show that this gives two significant advantages over existing techniques: improved accuracy in question answering and the capability to answer a broader range of questions, by integrating data from external sources. Then we investigate whether machine learning can predict each case study’s grade using this structured representation. The results provide accurate predictions for computer science impact case studies.

This article uses a “mystery client” approach and visits the websites of National Statistical Offices and international microdata libraries to assess whether foundational microdata sets for countries in the Middle East and North Africa region are collected, up to date, and made available to researchers. The focus is on population and economic censuses, price data and consumption, labor, health, and establishment surveys. The results show that about half of the expected core data sets are being collected and that only a fraction is made available publicly. As a consequence, many summary statistics, including national accounts and welfare estimates, are outdated and of limited relevance to decision-makers. Additional investments in microdata collection and publication of the data once collected are strongly advised.

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.

Chronic food insecurity remains a challenge globally, exacerbated by climate change-driven shocks such as droughts and floods. Forecasting food insecurity levels and targeting vulnerable households is apriority for humanitarian programming to ensure timely delivery of assistance. In this study, we propose to harness a machine learning approach trained on high-frequency household survey data to infer the predictors of food insecurity and forecast household level outcomes in near real-time. Our empirical analyses leverage the Measurement Indicators for Resilience Analysis (MIRA) data collection protocol implemented by Catholic Relief Services (CRS) in southern Malawi, a series of sentinel sites collecting household data monthly. When focusing on predictors of community-level vulnerability, we show that a random forest model outperforms other algorithms and that location and self-reported welfare are the best predictors of food insecurity. We also show performance results across several neural networks and classical models for various data modeling scenarios to forecast food security. We pose that problem as binary classification via dichotomization of the food security score based on two different thresholds, which results in two different positive class to negative class ratios. Our best performing model has an F1 of 81% and an accuracy of 83% in predicting food security outcomes when the outcome is dichotomized based on threshold 16 and predictor features consist of historical food security score along with 20 variables selected by artificial intelligence explainability frameworks. These results showcase the value of combining high-frequency sentinel site data with machine learning algorithms to predict future food insecurity outcomes.

Financial inclusion depends on providing adjusted services for citizens with disclosed vulnerabilities. At the same time, the financial industry needs to adhere to a strict regulatory framework, which is often in conflict with the desire for inclusive, adaptive, and privacy-preserving services. In this article we study how this tension impacts the deployment of privacy-sensitive technologies aimed at financial inclusion. We conduct a qualitative study with banking experts to understand their perspectives on service development for financial inclusion. We build and demonstrate a prototype solution based on open source decentralized identifiers and verifiable credentials software and report on feedback from the banking experts on this system. The technology is promising thanks to its selective disclosure of vulnerabilities to the full control of the individual. This supports GDPR requirements, but at the same time, there is a clear tension between introducing these technologies and fulfilling other regulatory requirements, particularly with respect to “Know Your Customer.” We consider the policy implications stemming from these tensions and provide guidelines for the further design of related technologies.

This paper investigates how socio-spatial aspects of creativity, operationalized as the causal relations between the built environment and perceived creativity in university campuses’ public spaces, are currently applied in practice. Moreover, it discusses practitioners’ perceptions regarding research-generated evidence on socio-spatial aspects of creativity according to three effectiveness aspects: credibility, relevance, and applicability. The “research-generated evidence” is herein derived from data-driven knowledge generated by multi-disciplinary methodologies (e.g., self-reported perceptions, participatory tools, geospatial analysis, observations). Through a thematic analysis of interviews with practitioners involved in the (re)development of campuses public spaces of inner-city campuses and science parks in Amsterdam, Utrecht, and Groningen. We concluded that socio-spatial aspects of creativity concepts were addressed only at the decision-making level for Utrecht Science Park. Correspondingly, while presented evidence was considered by most practitioners as relevant for practice, perceptions of credibility and applicability vary according to institutional goals, practitioners’ habits in practice, and their involvement in projects’ roles and phases. The newfound interrelationships between the three effectiveness aspects highlighted (a) the institutional fragmentation issues in campuses and public spaces projects, (b) the research-practice gap related to such projects, which occur beyond the university campuses’ context, and (c) insights on the relationship between evidence generated through research-based data-driven knowledge and urban planning practice, policy, and governance related to knowledge environments. We concluded that if research-generated evidence on socio-spatial aspects of creativity is to be integrated into the evidence-based practice of campuses’ public spaces, an alignment between researchers, multiple actors involved, policy framing, and goal achievements are fundamental.

Border artificial intelligence (AI)—biometrics-based AI systems used in border control contexts—proliferates as common tools in border securitization projects. Such systems classify some migrants as posing risks like identity fraud, other forms of criminality, or terrorism. From a human rights perspective, using such risk framings for algorithmically facilitated evaluations of migrants’ biometrics systematically calls into question whether these kinds of systems can be built to be trustworthy for migrants. This article provides a thought experiment; we use a bottom-up responsible design lens—the guidance-ethics approach—to evaluate if responsible, trustworthy Border AI might constitute an oxymoron. The proposed European AI Act only limits the use of Border AI systems by classifying such systems as high risk. In parallel with these AI regulatory developments, large-scale civic movements have emerged throughout Europe to ban the use of facial recognition technologies in public spaces to defend EU citizens’ privacy. The fact that such systems remain acceptable for states’ usage to evaluate migrants, we argue, insufficiently protects migrants’ lives. In part, we argue that this is due to regulations and ethical frameworks being top-down and technology driven by focusing more on the safety of AI systems than on the safety of migrants. We conclude that bordering technologies developed from a responsible design angle would entail the development of entirely different technologies. These would refrain from harmful sorting based on biometric identifications but would start from the premise that migration is not a societal problem.

Nigeria commenced its national foundational digital identity project in 2007 and had enrolled 60 million people by July 2021. The project, led by the National Identity Management Commission (NIMC), seeks to unify the country’s public and private functional identity databases, and aims to improve government services and national security. Although the enrolment process had encountered initial challenges such as the absence of enrolment centers in some communities across the country, enrolment for the biometric ID had proceeded without any significant public objection to its objectives. Following the EndSARS protests of October 2020, where youths protesting police violence and perceived poor governance were shot at by government security forces and protesters placed under surveillance, the government announced an updated national identity policy mandating citizens link their National Identity Number (NIN) with their SIM card information. For the first time, significant pockets of resistance arose against the national ID project by sections of the public who perceived the EndSARS violence as signaling a change in government behavior, and the updated ID policy as a mechanism for empowering government surveillance and authoritarianism. The resistance to the ID project marked a shift in public perception which threatens its future. This paper argues that mistrust in government data collection projects grows when data collection is perceived to be increasing government power to the detriment of human rights and freedom. It also puts forward a proposal on how to restore trust within the low-trust environment in Nigeria including the passage of a data protection law and amendments to the NIMC Act and Policies/Regulations, establishing Federated identity providers which give choices to end-users, and delinking the NIN from functional identity databases.

Digital identity systems are not devised for their own sake, rather they are developed by institutions as part of their pursuit of specific goals—such as economic, social, and developmental outcomes through enabling individual rights and facilitating access to basic services and entitlements. A growing number of organizations and institutions are advancing specific principles, frameworks, and “imaginaries” of what “good” digital identity looks like—yet it is often not clear how much influence they have or what their underlying worldview is to those designing, developing, and deploying these systems. This paper introduces sociopolitical configurations as a means of studying these underlying worldviews. Sociopolitical configurations combine elements from technological frames, expectations, and imaginations as well as developmental discourses to provide a basis for critically examining three key documents in this space.

Does digitalization reduce corruption? What are the integrity benefits of government digitalization? While the correlation between digitalization and corruption is well established, there is less actionable evidence on the integrity dividends of specific digitalization reforms on different types of corruption and the policy channels through which they operate. These linkages are especially relevant in high corruption risk environments. This article unbundles the integrity dividends of digital reforms undertaken by governments around the world, accelerated by the pandemic. It analyzes the rise of data-driven integrity analytics as promising tools in the anticorruption space deployed by tech-savvy integrity actors. It also assesses the broader integrity benefits of the digitalization of government services and the automation of bureaucratic processes, which contribute to reducing bribe solicitation risks by front-office bureaucrats. It analyzes in particular the impact of digitalization on social transfers. It argues that government digitalization can be an implicit yet effective anticorruption strategy, with subtler yet deeper effects, but there needs to be greater synergies between digital reforms and anticorruption strategies.

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.