This paper discusses the challenges and opportunities in accessing data to improve workplace relations law enforcement, with reference to minimum employment standards such as wages and working hours regulation. Our paper highlights some innovative examples of government and trade union efforts to collect and use data to improve the detection of noncompliance. These examples reveal the potential of data science as a compliance tool but also suggest the importance of realizing a data ecosystem that is capable of being utilized by machine learning applications. The effectiveness of using data and data science tools to improve workplace law enforcement is impacted by the ability of regulatory actors to access useful data they do not collect or hold themselves. Under “open data” principles, government data is increasingly made available to the public so that it can be combined with nongovernment data to generate value. Through mapping and analysis of the Australian workplace relations data ecosystem, we show that data availability relevant to workplace law compliance falls well short of open data principles. However, we argue that with the right protocols in place, improved data collection and sharing will assist regulatory actors in the effective enforcement of workplace laws.

The European public sector has for a long time tried to change its activities and its relation to the public through the production and provision of data and data-based technologies. Recent debates raised attention to data uses, through which societal value may be realized. However, often absent from these discussions is a conceptual and methodological debate on how to grasp and study such uses. This collection proposes a turn toward data practices—intended here as the analysis of data uses and policies, as they are articulated, understood, or turned into situated activities by different actors in specific contexts, involving organizational rules, socioeconomic factors, discourses, and artifacts. Through a mix of conceptual and methodological studies, the contributions explore how data-driven innovation within public institutions is understood, imagined, planned for, conducted, or assessed. The situations examined in this special issue show, for instance, that data initiatives carried out by different actors lack institutional rules to align data use to the actual needs of citizens; that data scientists are important moral actors whose ethical reasoning should be fostered; and that the materiality of data practices, such as databases, enables and constrains opportunities for public engagement. Collectively, the contributions offer new insights into what constitutes “data-driven innovation practices,” how different practices are assembled, and what their different political, moral, economic, and organizational implications are. The contributions focus on three particular topics of concern: the making of ethical and normative values in practice; organizational collaborations with and around data; and methodological innovations of studying data practices.

The U.S. federal government annually awards billions of dollars as contracts to procure different products and services from external businesses. Although the federal government’s immense purchasing power provides a unique opportunity to invest in the nation’s women-owned businesses (WOBs) and minority-owned businesses (MOBs) and advance the entrepreneurial dreams of many more Americans, gender and racial disparities in federal procurement are pervasive. In this study, we undertake a granular examination of these disparities by analyzing the data on 1,551,610 contracts awarded by 58 different federal government agencies. Specifically, we examine the representation of WOBs and MOBs in contracts with varying levels of STEM intensity and across 19 different contract categories, which capture the wide array of products and services purchased by the federal government. We show that contracts with higher levels of STEM intensity are associated with a lower likelihood of being awarded to WOBs and MOBs. Interestingly, the negative association between a contract’s STEM intensity and its likelihood to be awarded to MOBs is particularly salient for Black-, and Hispanic-owned businesses. Among the 19 categories of contracts, Black-owned businesses are more likely to receive contracts that are characterized by lower median pay levels. Collectively, these results provide data-driven evidence demonstrating the need to make a distinction between the different categories of MOBs and consider the type of products and services being procured while carrying out an examination of racial disparities in federal procurement.

Precision healthcare is an emerging field of science that utilizes an individual’s health information, context, and genetics to provide more personalized diagnostics and treatments. In this manuscript, we leverage that concept and present a group of machine learning models for precision gaming. These predictive models guide adolescents through best practices related to their health. The use case deployed is for girls in India through a mobile application released in three different Indian states. To evaluate the usability of the models, experiments are designed and data (demographic, behavioral, and health-related) are collected. The experimental results are presented and discussed.

The deployment of digital technologies in African cities, beyond improving service delivery, raises issues of digital inclusion, digital rights, and increasing spatial and social inequalities. As part of the African Cities Lab Summit 2023, we conducted a workshop with 20 multidisciplinary participants to explore issues related to the deployment of digital technologies in African cities. This research is a policy paper that addresses these issues and provides policy recommendations for local governments. It emphasizes the importance of inclusive digital infrastructure, regulations safeguarding vulnerable sectors, and governance ensuring citizens’ rights in the digital transformation. Focusing on transparency, equity, and collaboration with communities, local governments play a vital role in fostering inclusive digital transformation, essential for equitable and rights-centric smart cities in Africa.

This paper examines the potential role of network analysis in understanding the powerful elites that pose a significant threat to peace and state-building within post-conflict contexts. This paper makes a threefold contribution. First, it identifies a caveat in the scholarship surrounding international interventions, shedding light on shortcomings in their design and implementation strategies, and elucidating the influence these elites wield in the political and economic realms. Next, it delineates the essentials of the network analysis approach, addressing the information and data requirements and limitations inherent in its application in conflict environments. Finally, the paper provides valuable insights gleaned from the international operation in Guatemala known as the International Commission for Impunity in Guatemala, which specifically targeted illicit networks. The argument asserts that network analysis functions as a dual-purpose tool—serving as both a descriptive instrument to reveal, identify, and address the root causes of conflict and a predictive tool to enhance peace agreement implementation and improve decision-making. Simultaneously, it underscores the challenge of data analysis and translating network interventions into tangible real-life consequences for long-lasting results.

This paper explores citizens’ stances toward the use of artificial intelligence (AI) in public services in Norway. Utilizing a social contract perspective, the study analyzes the government–citizen relationship at macro, meso, and micro levels. A prototype of an AI-enabled public welfare service was designed and presented to 20 participants who were interviewed to investigate their stances on the described AI use. We found a generally positive attitude and identified three factors contributing to this: (a) the high level of trust in government (macro level); (b) the balanced value proposition between individual and collective needs (meso level); and (c) the reassurance provided by having humans in the loop and providing transparency into processes, data, and model’s logic (microlevel). The findings provide valuable insights into citizens’ stances for socially responsible AI in public services. These insights can inform policy and guide the design and implementation of AI systems in the public sector by foregrounding the government–citizen relationship.

A survey of Hong Kong residents finds that public support for government technology, as understood through the concept of smart cities, is associated with concept-awareness and official communications. The statistical analysis identifies moderating effects attributable to personal social media use and controls for personal ideological views about scope of government intervention and perceived political legitimacy of smart city policies. The study builds on a growing body of empirical scholarship about public support for government technology, while also addressing a practical trend in urban governance: the growing sophistication of technologies like artificial intelligence and their use in strengthening government capacities. The Hong Kong case exemplifies ambitious investments in technology by governments and, at the time of the survey, relatively high freedom of political expression. The study’s findings help refine theories about state-society relations in the rapidly evolving context of technology for public sector use.

This article provides a structured description of openly available news topics and forecasts for armed conflict at the national and grid cell level starting January 2010. The news topics, as well as the forecasts, are updated monthly at conflictforecast.org and provide coverage for more than 170 countries and about 65,000 grid cells of size 55 × 55 km worldwide. The forecasts rely on natural language processing (NLP) and machine learning techniques to leverage a large corpus of newspaper text for predicting sudden onsets of violence in peaceful countries. Our goals are a) to support conflict prevention efforts by making our risk forecasts available to practitioners and research teams worldwide, b) to facilitate additional research that can utilize risk forecasts for causal identification, and c) to provide an overview of the news landscape.

As the world has become more digitally dependent, questions of data governance, such as ethics, institutional arrangements, and statistical protection measures, have increased in significance. Understanding the economic contribution of investments in data sharing and data governance is highly problematic: outputs and outcomes are often widely dispersed and hard to measure, and the value of those investments is very context-dependent. The “Five Safes” is a popular data governance framework. It is used to design and critique data management strategies across the world and has also been used as a performance framework to measure the effectiveness of data access operations. We report on a novel application of the Five Safes framework to structure the economic evaluation of data governance. The Five Safes was designed to allow structured investigation into data governance. Combining this with more traditional logic models can provide an evaluation methodology that is practical, reproducible, and comparable. We illustrate this by considering the application of the combined logic model-Five Safes framework to data governance for agronomy investments in Ethiopia. We demonstrate how the Five Safes was used to generate the necessary context for a more traditional quantitative study, and consider lessons learned for the wider evaluation of data and data governance investments.

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.

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.

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.

Publicly funded data-driven innovation programmes frequently involve partnerships between small and medium enterprises (SMEs) and municipal authorities utilizing citizen data. The intention of these projects is to benefit citizens. However, few such projects achieve success or impact within the project timeframe. This may result in benefit accruing mainly to the SME partner, who gains both learning and data, engendering questions of data justice around whether citizen data are being exploited without sufficient benefit returning to citizens. Through case studies composed of interviews and document analysis, we examine how benefits for citizens are conceived and achieved in the publicly funded data-driven air quality projects Data Pitch and Smart Cities Innovation Framework Implementation. We find the differences between the programme funders’ policies had a clear influence on the citizen engagement elements. There are also a number of ways in which the desired citizen engagement and benefit becomes diluted, including through misalignment of incentives and focus, a lack of prioritization and ownership, and power imbalances between citizens and the other actors in the quadruple helix model. To retain the focus on ensuring citizens benefit from data-driven innovation programmes using citizen data, we propose the use of data Justice plans. More work is required to specify the content and mechanisms of such plans for application in such programmes.

This paper investigates the role of motorized three-wheelers (MTW) in urban mobility within popular transport, a demand-responsive and unscheduled mode of transportation provided by self-organized small operators frequently operating in grey areas of regulation. Although popular transport is the primary mobility option for millions worldwide, knowledge about its users, operation, and environmental and social impacts remains scarce. This paper sheds light on some of the features and impacts of popular MTW, focusing on two case studies in the Caribbean with different scales and urban trajectories: Puerto Viejo, Costa Rica, and Soledad in Colombia. We explored the relationship between MTW and fragmentation–(in)accessibility–exclusion in these cities, drawing on a framework connecting these concepts in the Latin American and Caribbean context. Using primary data from qualitative and quantitative methods, the paper examines the distribution of inhibitors or enablers of accessibility within the context of unequal, splintered, and fragmented transport and communication infrastructures. Additionally, the environmental impact of MTW in terms of CO2 and PM2.5 emissions is assessed using field data from low-cost sensors. The paper argues that planning for just urban mobility necessitates considering the ecological consequences of various transportation modes and their social consequences and potential for participation and inclusion. The applied methodology introduces low-cost, replicable, and scalable data production and analysis techniques, contributing to future research on sustainable and just mobility in resource-limited urban areas.

Nowadays public policymakers are offered with opportunities to take data-driven evidence-based decisions by analyzing the very large volumes of policy-related data that are generated through different channels (e.g., e-services, mobile apps, social media). Machine learning (ML) and artificial intelligence (AI) tehcnologies ease and automate the analysis of large policy-related datasets, which helps policymakers to realize a shift toward data-driven decisions. Nevertheless, the deployment and use of AI tools for public policy development is also associated with significant technical, political, and operation challenges. For instance, AI-based policy development solutions must be transparent and explainable to policymakers, while at the same time adhering to the mandates of emerging regulations such as the AI Act of the European Union. This paper introduces some of the main technical, operational, regulatory compliance challenges of AI-based policymaking. Accordingly, it introduces technological solutions for overcoming them, including: (i) a reference architecture for AI-based policy development, (ii) a virtualized cloud-based tool for the specification and implementation of ML-based data-driven policies, (iii) a ML framework that enables the development of transparent and explainable ML models for policymaking, and (iv) a set of guidelines for using the introduced technical solutions to achieve regulatory compliance. The paper ends up illustrating the validation and use of the introduced solutions in real-life public policymaking cases for various local governments.

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

The use of computer technology to automate the enforcement of law is a promising alternative to simplify bureaucratic procedures. However, careless automation might result in an inflexible and dehumanized law enforcement system driven by algorithms that do not account for the particularities of individuals or minorities. In this article, we argue that hybrid smart contracts deployed to monitor rather than blindly enforce regulations can be used to add flexibility. Enforcement is a suitable alternative only when prevention is strictly necessary; however, we argue that in many situations a corrective approach based on monitoring is more flexible and suitable. To add more flexibility, the hybrid smart contract can be programmed to stop to request the intervention of a human or of a group of them when human judgment is needed.

This study sought to establish the elements that constitute comprehensive legal and regulatory landscape for successful digital identity system establishment and implementation. Subsequently, the study sought to assess whether these elements were present in the establishment and implementation of the National Integrated Identity Management System (NIIMS) in Kenya. The study adopted a qualitative approach, data was obtained firstly, through literature review that provided background information to the study. Secondly, semi structured interviews were undertaken on purposively selected key informants. The study established that the elements that constitute a robust legal and regulatory framework for digital identity (ID) establishment and implementation include presence of a constitutional provision on the right to privacy; existence of a digital ID law governing the establishment of the system; amendment of laws relating to the registration of persons; existence of a data protection law; existence of an overarching law governing the digital economy among others. Largely, most of these elements were present in Kenya. However, the legislative approach adopted in crafting digital ID law in Kenya was wanting. This has undermined effective implementation of the NIIM system by among other things eroding public confidence in the system. The study concluded that effective operation of the system hinged on the existence of a robust and comprehensive legal and regulatory framework that will engender users’ trust in the system. In this regard, the study recommended review of the existing legal framework to ensure that it underpins both the foundational and functional aspects of the NIIM system.