To harness the promises of digital transformation, different players take different paths. Departing from corporate-driven (e.g., the United States) and state-led (e.g., China) approaches, in various documents, the European Union states its goal to establish a citizen-centric data ecosystem. However, it remains contentious the extent to which the envisioned digital single market can enable the creation of public value and empower citizens. As an alternative, in this article, we argue in favor of a fair data ecosystem, defined as an approach capable of representing and keep in balance the data interests of all actors, while maintain a collective outlook. We build such ecosystem around data commons—as a third path to market and state approaches to the managing of resources—coupled with open data (OD) frameworks and spatial data infrastructures (SDIs). Indeed, based on literature, we claim that these three regimes complement each other, with OD and SDIs supplying infrastructures and institutionalization to data commons’ limited replicability and scalability. This creates the preconditions for designing the main roles, rules, and mechanisms of a data republic, as a possible enactment of a fair data ecosystem. While outlining here its main traits, the testing of the data republic model is open for further research.

This work explores the use of Trusted Research Environments for the secure analysis of sensitive, record-level data on local coronavirus disease-2019 (COVID-19) inequalities and economic vulnerabilities. The Local Data Spaces (LDS) project was a targeted rapid response and cross-disciplinary collaborative initiative using the Office for National Statistics’ Secure Research Service for localized comparison and analysis of health and economic outcomes over the course of the COVID-19 pandemic. Embedded researchers worked on co-producing a range of locally focused insights and reports built on secure secondary data and made appropriately open and available to the public and all local stakeholders for wider use. With secure infrastructure and overall data governance practices in place, accredited researchers were able to access a wealth of detailed data and resources to facilitate more targeted local policy analysis. Working with data within such infrastructure as part of a larger research project involved advanced planning and coordination to be efficient. As new and novel granular data resources become securely available (e.g., record-level administrative digital health records or consumer data), a range of local policy insights can be gained across issues of public health or local economic vitality. Many of these new forms of data however often come with a large degree of sensitivity around issues of personal identifiability and how the data is used for public-facing research and require secure and responsible use. Learning to work appropriately with secure data and research environments can open up many avenues for collaboration and analysis.

Bribery for access to public goods and services remains a widespread and seemingly innocuous practice which disproportionately targets the poor and helps keep them poor. Furthermore, its aggregate effects erode the legitimacy of government institutions and their capacity to fairly administer public goods and services as well as protection under the law. Drawing on original evidence using social norms methodology, this research tests underlying beliefs and expectations which sustain persistent forms of bribery and draws attention to the presence of pluralistic ignorance and consequent collective action problems. With examples focused on bribery in traffic law enforcement, healthcare, and education—three critical areas where bribery is often identified as an entrenched practice—this article contributes new evidence of: (a) the presence of pluralistic ignorance, a common social comparison error, surrounding bribery behavior; (b) differing social evaluations of bribe-solicitation; and finally, (c) how this context might exacerbate collective action problems. This empirical case study of Nigeria shows that even though more people are likely to be directly affected by bribery during routine interactions with public officials and institutions and many believe this practice is wrong, most people incorrectly believe that others in their community tolerate or even accept bribery behavior.

The use of artificial intelligence and algorithmic decision-making in public policy processes is influenced by a range of diverse drivers. This article provides a comprehensive view of 13 drivers and their interrelationships, identified through empirical findings from the taxation and social security domains in Belgium. These drivers are organized into five hierarchical layers that policy designers need to focus on when introducing advanced analytics in fraud detection: (a) trust layer, (b) interoperability layer, (c) perceived benefits layer, (d) data governance layer, and (e) digital governance layer. The layered approach enables a holistic view of assessing adoption challenges concerning new digital technologies. The research uses thematic analysis and interpretive structural modeling.

Governments all over the world are rapidly embracing digital technologies for information collection, governance, and social control. Recent studies suggest citizens may accept or even support digital surveillance. By using an online survey dataset on public opinion about facial recognition technology, contact tracing apps, and the social credit system in China, Germany, the US, and the UK, this article shows that these studies have overlooked a small yet significant group of digital technology doubters. Our results show that while up to 10% of Chinese citizens belong to the group of “digital doubters,” this group is the largest in Germany with 30% of citizens. The US and the UK are in the middle with approximately 20%. While citizens who belong to this group of digital doubters worry about privacy and surveillance issues, their attitudes can also be explained by them not being convinced of the benefits of digital technologies, including improved efficiency, security, or convenience. We find that the more citizens lack trust in their government, the more likely they are to belong to the group of digital doubters. Our findings demonstrate that in both democratic and authoritarian states, there are citizens opposing the adoption of certain digital technologies. This underscores the importance of initiating societal debate to determine the appropriate regulations that align with these societal preferences.

Many international organisations have recently acknowledged the significance of whistleblowing in preventing institutional corruption, particularly in the public sector. Likewise, many countries have enacted whistleblower protection laws, though a robust whistleblower protection system certainly requires much more than legislation. One challenge in developing effective protection systems is finding empirical evidence to evaluate existing systems. Can we measure the effectiveness of whistleblower protection systems accross different countries? What conditions do we need to make the whistleblower protection system work effectively in the public sector? This paper investigates two cases: South Korea and the Republic of Kosovo. South Korean data comes from the Anti-Corruption and Civil Rights Commission of South Korea, while its counterpart from Kosovo comes from a survey of 400 public officials about whistleblower protection. By analysing both datasets, this paper creates a new index that evaluates the effectiveness of whistleblower protection. Composed of quantitative and qualitative sub-indices, the index serves as a digital comparison tool for assessing whistleblower protection systems across different countries and at different times. In addition to enacting high-quality laws, this index identifies several additional measures that can strengthen whistleblower protection systems in the public sector.

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.

Data has become a critical trans-national and cross-border resource. Yet, the lack of a well-defined approach to using it poses challenges to harnessing its value. This article explores the increasing importance of global data governance due to the rapid growth of data, and the need for responsible data practices. The purpose of this paper is to compare approaches and identify patterns in the emergent data governance ecosystem within sectors close to the international development field, ultimately presenting key takeaways and reflections on when and why a global data governance framework may be needed. Overall, the paper provides information about the conditions when a more holistic, coordinated transnational approach to data governance may be needed to responsibly manage the global flow of data. The report does this by (a) considering conditions specified by the literature that may be conducive to global data governance, and (b) analyzing and comparing existing frameworks, specifically investigating six key elements: purpose, principles, anchoring documents, data description and lifecycle, processes, and practices. The article closes with a series of final recommendations, which include adopting a broader concept of data stewardship to reconcile data protection and promotion, focusing on responsible reuse of data to unlock socioeconomic value, harmonizing meanings to operationalize principles, incorporating global human rights frameworks to provide common North Stars, unifying key definitions of data, adopting a data lifecycle approach, incorporating participatory processes and collective agency, investing in new professions with specific roles, improving accountability through oversight and compliance mechanisms, and translating recommendations into practical tools.

The “budgeting for SDGs”–B4SDGs–paradigm seeks to coordinate the budgeting process of the fiscal cycle with the sustainable development goals (SDGs) set by the United Nations. Integrating the goals into public financial management systems is crucial for an effective alignment of national development priorities with the objectives set in the 2030 Agenda. Within the dynamic process defined in the B4SDGs framework, the step of SDG budget tagging represents a precondition for subsequent budget diagnostics. However, developing a national SDG taxonomy requires substantial investment in terms of time, human, and administrative resources. Such costs are exacerbated in least-developed countries, which are often characterized by a constrained institutional capacity. The automation of SDG budget tagging could represent a cost-effective solution. We use well-established text analysis and machine learning techniques to explore the scope and scalability of automatic labeling budget programs within the B4SDGs framework. The results show that, while our classifiers can achieve great accuracy, they face limitations when trained with data that is not representative of the institutional setting considered. These findings imply that a national government trying to integrate SDGs into its planning and budgeting practices cannot just rely solely on artificial intelligence (AI) tools and off-the-shelf coding schemes. Our results are relevant to academics and the broader policymaker community, contributing to the debate around the strengths and weaknesses of adopting computer algorithms to assist decision-making processes.

Statistical profiling of job seekers is an attractive option to guide the activities of public employment services. Many hope that algorithms will improve both efficiency and effectiveness of employment services’ activities that are so far often based on human judgment. Against this backdrop, we evaluate regression and machine-learning models for predicting job-seekers’ risk of becoming long-term unemployed using German administrative labor market data. While our models achieve competitive predictive performance, we show that training an accurate prediction model is just one element in a series of design and modeling decisions, each having notable effects that span beyond predictive accuracy. We observe considerable variation in the cases flagged as high risk across models, highlighting the need for systematic evaluation and transparency of the full prediction pipeline if statistical profiling techniques are to be implemented by employment agencies.

Within the last decade, online sustainability knowledge-action platforms have proliferated. We surveyed 198 sustainability-oriented sites and conducted a review of 41 knowledge-action platforms, which we define as digital tools that advance sustainability through organized activities and knowledge dissemination. We analyzed platform structure and functionality through a systematic coding process based on key issues identified in three bodies of literature: (a) the emergence of digital platforms, (b) the localization of the sustainable development goals (SDGs), and (c) the importance of multi-level governance to sustainability action. While online collaborative tools offer an array of resources, our analysis indicates that they struggle to provide context-sensitivity and higher-level analysis of the trade-offs and synergies between sustainability actions. SDG localization adds another layer of complexity where multi-level governance, actor, and institutional priorities may generate tensions as well as opportunities for intra- and cross-sectoral alignment. On the basis of our analysis, we advocate for the development of integrative open-source and dynamic global online data management tools that would enable the monitoring of progress and facilitate peer-to-peer exchange of ideas and experience among local government, community, and business stakeholders. We argue that by showcasing and exemplifying local actions, an integrative platform that leverages existing content from multiple extant platforms through effective data interoperability can provide additional functionality and significantly empower local actors to accelerate local to global actions, while also complex system change.

The coronavirus disease-2019 (COVID-19) pandemic and the mobility restrictions governments imposed to prevent its spread changed the cities’ ways of living. Transport systems suffered the consequences of the falling travel demand, and readjustments were made in many cities to prevent the complete shutdown of services. In Córdoba, the second largest city in Argentina, the Municipality dictated route cuts and reduced frequencies to sustain the buses and trolleys system. In 2022, Martinazzo and Falavigna assessed potential accessibility to hospitals before (2019) and during the pandemic (2021). Overall, the study indicated that average travel times increased by 20% and that the gap between less vulnerable and more vulnerable population quintiles reached almost 8 points. In this paper, potential accessibility to public hospitals in 2022 and 2023 is calculated using Martinazzo and Falavigna’s (2022) work as a baseline to compare, considering that neither cutting the services during the pandemic nor recovering the service after the pandemic the Municipality performed an accessibility assessment. The main results showed that, despite the system having almost recovered its extension by 2023, it maintained the regressive tendency between less vulnerable and more vulnerable population quintiles, as the difference in average travel time between these two groups reached up to 14 min, while the cumulative opportunities measure for the high-income groups was up to 68% higher than the most vulnerable households.

Several governmental organizations all over the world aim for algorithmic accountability of artificial intelligence systems. However, there are few specific proposals on how exactly to achieve it. This article provides an extensive overview of possible transparency and inspectability mechanisms that contribute to accountability for the technical components of an algorithmic decision-making system. Following the different phases of a generic software development process, we identify and discuss several such mechanisms. For each of them, we give an estimate of the cost with respect to time and money that might be associated with that measure.

Since 1998, Bogotá has consistently made substantial efforts to foster the bicycle’s role as a primary mode of transportation. Recent years have witnessed a compelling aspiration for the city to ascend as the “bicycle capital of the world,” evident in its accomplishment of 6.6% of daily trips completed by bicycle in 2019. This achievement translates to 880.367 daily cycling journeys (District Secretariat of Mobility of Bogotá, 2019). These statistics surpass regional benchmarks; for instance, other capital cities such as Santiago de Chile account for 510.569 bicycle trips, Mexico City for 433.981, and Rio de Janeiro for 217.000 (Ríos et al., 2015). Despite this progress, Bogotá lacks a comprehensive evaluation of both infrastructure quality and the user experience while cycling.

This translational research article aimed to explore this gap by delving into the integration of user perceptions and experiences within the policy formulation process. This strategic approach is poised to enhance cycling’s allure as a mode of transportation for prospective cyclists while simultaneously maximizing the efficiency of investments in cycling infrastructure.

Self-sovereign identity (SSI) is an emerging and promising concept that enables users to control their identity while enhancing security and privacy compared to other identity management (IDM) approaches. Despite the recent advancements in SSI technologies, federated identity management (FIDM) systems continue to dominate the IDM market. Selecting an IDM to implement for a specific application is a complex task that requires a thorough understanding of the potential external cyber risks. However, existing research scarcely compares SSI and FIDM from the perspective of these external threats. In response to this gap, our article provides an attack surface analysis focused solely on external threats for both systems. This analysis can serve as a reference to compare the relevant security and privacy risks associated with these external threats. The threat landscapes of external attackers were systematically synthesized from the main components and functionalities of the common standards and designs. We further present a use case analysis that applies this attack surface analysis to compare the external cyber risks of the two systems in detail when managing cross-border identity between European countries. This work can be particularly useful for considering a more secure design for future IDM applications, taking into account the landscape of external threats.

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.”

Understanding the spatio-temporal patterns of users’ travel behavior on public transport (PT) systems is essential for more assertive transit planning. With this in mind, the aim of this article is to diagnose the spatial and temporal travel patterns of users of Fortaleza’s PT network, which is a trunk-feeder network whose fares are charged by a tap-on system. To this end, 20 databases were used, including global positioning system, user registration, and PT smart card data from November 2018, prior to the pandemic. The data set was processed and organized into a database with a relational model and an Extraction, Transformation, and Loading process. A data mining approach based on Machine Learning models was applied to evaluate travel patterns. As a result, it was observed that users’ first daily use has a higher percentage of spatial and temporal patterns when compared to their last daily use. In addition, users rarely show spatial and temporal patterns at the same time.