Enabling private sector trust stands as a critical policy challenge for the success of the EU Data Governance Act and Data Act in promoting data sharing to address societal challenges. This paper attributes the widespread trust deficit to the unmanageable uncertainty that arises from businesses’ limited usage control to protect their interests in the face of unacceptable perceived risks. For example, a firm may hesitate to share its data with others in case it is leaked and falls into the hands of business competitors. To illustrate this impasse, competition, privacy, and reputational risks are introduced, respectively, in the context of three suboptimal approaches to data sharing: data marketplaces, data collaboratives, and data philanthropy. The paper proceeds by analyzing seven trust-enabling mechanisms comprised of technological, legal, and organizational elements to balance trust, risk, and control and assessing their capacity to operate in a fair, equitable, and transparent manner. Finally, the paper examines the regulatory context in the EU and the advantages and limitations of voluntary and mandatory data sharing, concluding that an approach that effectively balances the two should be pursued.

Several African countries are developing artificial intelligence (AI) strategies and ethics frameworks with the goal of accelerating responsible AI development and adoption. However, many of these governance actions are emerging without consideration for their suitability to local contexts, including whether the proposed policies are feasible to implement and what their impact may be on regulatory outcomes. In response, we suggest that there is a need for more explicit policy learning, by looking at existing governance capabilities and experiences related to algorithms, automation, data, and digital technology in other countries and in adjacent sectors. From such learning, it will be possible to identify where existing capabilities may be adapted or strengthened to address current AI-related opportunities and risks. This paper explores the potential for learning by analysing existing policy and legislation in twelve African countries across three main areas: strategy and multi-stakeholder engagement, human dignity and autonomy, and sector-specific governance. The findings point to a variety of existing capabilities that could be relevant to responsible AI; from existing model management procedures used in banking and air quality assessment to efforts aimed at enhancing public sector skills and transparency around public–private partnerships, and the way in which existing electronic transactions legislation addresses accountability and human oversight. All of these point to the benefit of wider engagement on how existing governance mechanisms are working, and on where AI-specific adjustments or new instruments may be needed.

Digital identity systems are promoted with the promise of great benefit and inclusion. The case of the Ugandan digital identity system demonstrates that the impact of digital identity systems is not only positive but also has negative impacts, significantly affecting human lives for the worse. The impact on the human lives of digital identity systems can be assessed by multiple frameworks. A specific framework that has been mentioned is the capabilities approach (CA). This article demonstrates that the CA is a framework to assess the impact on human lives that can be operationalized for technology and information and communication technology, including digital identity systems. Further research is required to compare the CA with other candidate evaluation frameworks.

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

The advent of smart and digital cities is bringing data to the forefront as a critical resource for addressing the multifaceted transitions faced by African cities from rapid urbanization to the climate crisis. However, this commentary highlights the formidable considerations that must be addressed to realize the potential of data-driven urban planning and management. We argue that data should be viewed as a tool, not a panacea, drawing from our experience in modeling and mapping the accessibility of transport systems in Accra and Kumasi, Ghana. We identify five key considerations, including data choice, imperfections, resource intensity, validation, and data market dynamics, and propose three actionable points for progress: local data sharing, centralized repositories, and capacity-building. While our focus is on Kumasi and Accra, the considerations discussed are relevant to cities across the African continent.

Corruption has pervasive effects on economic development and the well-being of the population. Despite being crucial and necessary, fighting corruption is not an easy task because it is a difficult phenomenon to measure and detect. However, recent advances in the field of artificial intelligence may help in this quest. In this article, we propose the use of machine-learning models to predict municipality-level corruption in a developing country. Using data from disciplinary prosecutions conducted by an anti-corruption agency in Colombia, we trained four canonical models (Random Forests, Gradient Boosting Machine, Lasso, and Neural Networks), and ensemble their predictions, to predict whether or not a mayor will commit acts of corruption. Our models achieve acceptable levels of performance, based on metrics such as the precision and the area under the receiver-operating characteristic curve, demonstrating that these tools are useful in predicting where misbehavior is most likely to occur. Moreover, our feature-importance analysis shows us which groups of variables are most important in predicting corruption.

This article uses data from several publicly available databases to show that the distribution of intellectual property for frontier technologies, including those useful for sustainable development, is very highly skewed in favor of a handful of developed countries. The intellectual property rights (IPR) regime as it exists does not optimize the global flow of technology and know-how for the attainment of the sustainable development goals and is in need of updating. Some features of the Fourth Industrial Revolution imply that the current system of patents is even more in need of reform than before. COVID-19 vaccines and therapies and the vast inequality in access to these has highlighted the costs of inaction. We recommend several policy changes for the international IPR regime. Broadly, these fall into three categories: allowing greater flexibility for developing countries, reassessing the appropriateness of patents for technologies that may be considered public goods, and closing loopholes that allow for unreasonable intellectual property protections.

This study uses anonymized GPS traces to explore travel patterns within six suburban zones and a central area in Mexico City. The descriptive analysis presented in this paper profiles trips by distance and investigates their distribution within each zone. It examines the prevalence of local trips, walkability, and the availability and spread of entertainment sites within 15-min isochrones accessible by foot, bicycle, transit, and private vehicle. Notably, the central zone boasts diverse entertainment offerings, commendable walkability, and a substantial proportion of short and long trips. It is found that GPS traces are within their home. However, the share of long trips for the inhabitants of central zones is considerably more significant than that for the suburbs. The study highlights suburban zones that could benefit from governmental intervention to enhance transportation and pedestrian conditions. Additionally, it identifies other suburban zones that resemble the central areas in terms of walkability, trip distribution by distances, and the accessibility of entertainment places.

Open data promises various benefits, including stimulating innovation, improving transparency and public decision-making, and enhancing the reproducibility of scientific research. Nevertheless, numerous studies have highlighted myriad challenges related to preparing, disseminating, processing, and reusing open data, with newer studies revealing similar issues to those identified a decade prior. Several researchers have proposed the open data ecosystem (ODE) as a lens for studying and devising interventions to address these issues. Since actors in the ecosystem are individually and collectively impacted by the sustainability of the ecosystem, all have a role in tackling the challenges in the ODE. This paper asks what the contributions of open data intermediaries may be in addressing these challenges. Open data intermediaries are third-party actors providing specialized resources and capabilities to (i) enhance the supply, flow, and/or use of open data and/or (ii) strengthen the relationships among various open data stakeholders. They are critical in ensuring the flow of resources within the ODE. Through semi-structured interviews and a validation exercise in the European Union context, this study explores the potential contribution of open data intermediaries and the specific ODE challenges they may address. This study identified 20 potential contributions, addressing 27 challenges. The findings of this study pave the way for further inquiry into the internal incentives (viable business models) and external incentives (policies and regulations) to direct the contributions of open data intermediaries toward addressing challenges in the ODE.

For smart cities, data-driven innovation promises societal benefits and increased well-being for residents and visitors. At the same time, the deployment of data-driven innovation poses significant ethical challenges. Although cities and other public-sector actors have increasingly adopted ethical principles, employing them in practice remains challenging. In this commentary, we use a virtue-based approach that bridges the gap between abstract principles and the daily work of practitioners who engage in and with data-driven innovation processes. Inspired by Aristotle, we describe practices of data-driven innovation in a smart city applying the concepts of virtue and phronêsis, meaning good judgment of and sensitivity to ethical issues. We use a dialogic case-study approach to study two cases of data-driven innovation in the city of Helsinki. We then describe as an illustration of how our approach can help bridge the gap between concrete practices of data-driven innovation and high-level principles. Overall, we advance a theoretically grounded, virtue-based approach, which is practice oriented and linked to the daily work of data scientists and other practitioners of data-driven innovation. Further, this approach helps understand the need for and importance of individual application of phronêsis, which is particularly important in public-sector organizations that can experience gaps between principle and practice. This importance is further intensified in cases of data-driven innovation in which, by definition, novel and unknown contexts are explored.

Supreme audit institutions (SAIs) are touted as an integral component to anticorruption efforts in developing nations. SAIs review governmental budgets and report fiscal discrepancies in publicly available audit reports. These documents contain valuable information on budgetary discrepancies, missing resources, or may even report fraud and corruption. Existing research on anticorruption efforts relies on information published by national-level SAIs while mostly ignoring audits from subnational SAIs because their information is not published in accessible formats. I collect publicly available audit reports published by a subnational SAI in Mexico, the Auditoria Superior del Estado de Sinaloa, and build a pipeline for extracting the monetary value of discrepancies detected in municipal budgets. I systematically convert scanned documents into machine-readable text using optical character recognition, and I then train a classification model to identify paragraphs with relevant information. From the relevant paragraphs, I extract the monetary values of budgetary discrepancies by developing a named entity recognizer that automates the identification of this information. In this paper, I explain the steps for building the pipeline and detail the procedures for replicating it in different contexts. The resulting dataset contains the official amounts of discrepancies in municipal budgets for the state of Sinaloa. This information is useful to anticorruption policymakers because it quantifies discrepancies in municipal spending potentially motivating reforms that mitigate misappropriation. Although I focus on a single state in Mexico, this method can be extended to any context where audit reports are publicly available.

Current research on data in policy has primarily focused on street-level bureaucrats, neglecting the changes in the work of policy advisors. This research fills this gap by presenting an explorative theoretical understanding of the integration of data, local knowledge and professional expertise in the work of policy advisors. The theoretical perspective we develop builds upon Vickers’s (1995, The Art of Judgment: A Study of Policy Making, Centenary Edition, SAGE) judgments in policymaking. Empirically, we present a case study of a Dutch law enforcement network for preventing and reducing organized crime. Based on interviews, observations, and documents collected in a 13-month ethnographic fieldwork period, we study how policy advisors within this network make their judgments. In contrast with the idea of data as a rationalizing force, our study reveals that how data sources are selected and analyzed for judgments is very much shaped by the existing local and expert knowledge of policy advisors. The weight given to data is highly situational: we found that policy advisors welcome data in scoping the policy issue, but for judgments more closely connected to actual policy interventions, data are given limited value.

Social network analysis is known to provide a wealth of insights relevant to many aspects of policymaking. Yet, the social data needed to construct social networks are not always available. Furthermore, even when they are, interpreting such networks often relies on extraneous knowledge. Here, we propose an approach to infer social networks directly from the texts produced by actors and the terminological similarities that these texts exhibit. This approach relies on fitting a topic model to the texts produced by these actors and measuring topic profile correlations between actors. This reveals what can be called “hidden communities of interest,” that is, groups of actors sharing similar semantic contents but whose social relationships with one another may be unknown or underlying. Network interpretation follows from the topic model. Diachronic perspectives can also be built by modeling the networks over different time periods and mapping genealogical relationships between communities. As a case study, the approach is deployed over a working corpus of academic articles (domain of philosophy of science; N=16,917).

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.

Generative artificial intelligence (GenAI) has gained significant popularity in recent years. It is being integrated into a variety of sectors for its abilities in content creation, design, research, and many other functionalities. The capacity of GenAI to create new content—ranging from realistic images and videos to text and even computer code—has caught the attention of both the industry and the general public. The rise of publicly available platforms that offer these services has also made GenAI systems widely accessible, contributing to their mainstream appeal and dissemination. This article delves into the transformative potential and inherent challenges of incorporating GenAI into the domain of judicial decision-making. The article provides a critical examination of the legal and ethical implications that arise when GenAI is used in judicial rulings and their underlying rationale. While the adoption of this technology holds the promise of increased efficiency in the courtroom and expanded access to justice, it also introduces concerns regarding bias, interpretability, and accountability, thereby potentially undermining judicial discretion, the rule of law, and the safeguarding of rights. Around the world, judiciaries in different jurisdictions are taking different approaches to the use of GenAI in the courtroom. Through case studies of GenAI use by judges in jurisdictions including Colombia, Mexico, Peru, and India, this article maps out the challenges presented by integrating the technology in judicial determinations, and the risks of embracing it without proper guidelines for mitigating potential harms. Finally, this article develops a framework that promotes a more responsible and equitable use of GenAI in the judiciary, ensuring that the technology serves as a tool to protect rights, reduce risks, and ultimately, augment judicial reasoning and access to justice.

A broad gap exists between “God’s eye” transit maps from above that experts draw and how domestic workers map their commutes in Bogotá and Medellín, Colombia, through a street-level approach. Based on fieldwork conducted in both cities between 2017 and 2018, including interviews, participant observation, and social cartography, this translational article brings domestic workers’ understanding of the city they traverse daily vis-à-vis how experts conceive modern and rational public transportation systems. Delving into the literature on cartography, the Right to the City (RtC), and feminist geography, the study analyzes this gap and finds how it limits an effective RtC for this massive group of female commuters. It further provides public policy recommendations to address the gap and ensure RtC for all.

People rely extensively on online social networks (OSNs) in Africa, which aroused cyber attackers’ attention for various nefarious actions. This global trend has not spared African online communities, where the proliferation of OSNs has provided new opportunities and challenges. In Africa, as in many other regions, a burgeoning black-market industry has emerged, specializing in the creation and sale of fake accounts to serve various purposes, both malicious and deceptive. This paper aims to build a set of machine-learning models through feature selection algorithms to predict the fake account, increase performance, and reduce costs. The suggested approach is based on input data made up of features that describe the profiles being investigated. Our findings offer a thorough comparison of various algorithms. Furthermore, compared to machine learning without feature selection and Boruta, machine learning employing the suggested genetic algorithm-based feature selection offers a clear runtime advantage. The final prediction model achieves AUC values between 90% and 99.6%. The findings showed that the model based on the features chosen by the GA algorithm provides a reasonable prediction quality with a small number of input variables, less than 31% of the entire feature space, and therefore permits the accurate separation of fake from real users. Our results demonstrate exceptional predictive accuracy with a significant reduction in input variables using the genetic algorithm, reaffirming the effectiveness of our approach.

This study focuses on the practicalities of establishing and maintaining AI infrastructure, as well as the considerations for responsible governance by investigating the integration of a pre-trained large language model (LLM) with an organisation’s knowledge management system via a chat interface. The research adopts the concept of “AI as a constituted system” to emphasise the social, technical, and institutional factors that contribute to AI’s governance and accountability. Through an ethnographic approach, this article details the iterative processes of negotiation, decision-making, and reflection among organisational stakeholders as they develop, implement, and manage the AI system. The findings indicate that LLMs can be effectively governed and held accountable to stakeholder interests within specific contexts, specifically, when clear institutional boundaries facilitate innovation while navigating the risks related to data privacy and AI misbehaviour. Effective constitution and use can be attributed to distinct policy creation processes to guide AI’s operation, clear lines of responsibility, and localised feedback loops to ensure accountability for actions taken. This research provides a foundational perspective to better understand algorithmic accountability and governance within organisational contexts. It also envisions a future where AI is not universally scaled but consists of localised, customised LLMs tailored to stakeholder interests.