For decades, proponents of the Internet have promised that it would one day provide a seamless way for everyone in the world to communicate with each other, without introducing new boundaries, gatekeepers, or power structures. What happened? This article explores the system-level characteristics of a key design feature of the Internet that helped it to achieve widespread adoption, as well as the system-level implications of certain patterns of use that have emerged over the years as a result of that feature. Such patterns include the system-level acceptance of particular authorities, mechanisms that promote and enforce the concentration of power, and network effects that implicitly penalize those who do not comply with decisions taken by privileged actors. We provide examples of these patterns and offer some key observations, toward the development of a general theory of why they emerged despite our best efforts, and we conclude with some suggestions on how we might mitigate the worst outcomes and avoid similar experiences in the future.

The promised merits of data-driven innovation in general and algorithmic systems in particular hardly need enumeration. However, as decision-making tasks are increasingly delegated to algorithmic systems, this raises questions about accountability. These pressing questions of algorithmic accountability, particularly with regard to data-driven innovation in the public sector, deserve ample scholarly attention. Therefore, this paper brings together perspectives from governance studies and critical algorithm studies to assess how algorithmic accountability succeeds or falls short in practice and analyses the Dutch System Risk Indication (SyRI) as an empirical case. Dissecting a concrete case teases out to which degree archetypical accountability practices and processes function in relation to algorithmic decision-making processes, and which new questions concerning algorithmic accountability emerge therein. The case is approached through the analysis of “scavenged” material. It was found that while these archetypical accountability processes and practices can be incredibly productive in dealing with algorithmic systems they are simultaneously at risk. The current accountability configurations hinge predominantly on the ex ante sensitivity and responsiveness of the political fora. When these prove insufficient, mitigation in medias res/ex post is very difficult for other actants. In part, this is not a new phenomenon, but it is amplified in relation to algorithmic systems. Different fora ask different kinds of medium-specific questions to the actor, from different perspectives with varying power relations. These algorithm-specific considerations relate to the decision-making around an algorithmic system, their functionality, and their deployment. Strengthening ex ante political accountability fora to these algorithm-specific considerations could help mitigate this.

Open government and open data are often presented as the Asterix and Obelix of modern government—one cannot discuss one, without involving the other. Modern government, in this narrative, should open itself up, be more transparent, and allow the governed to have a say in their governance. The usage of technologies, and especially the communication of governmental data, is then thought to be one of the crucial instruments helping governments achieving these goals. Much open government data research, hence, focuses on the publication of open government data, their reuse, and re-users. Recent research trends, by contrast, divert from this focus on data and emphasize the importance of studying open government data in practice, in interaction with practitioners, while simultaneously paying attention to their political character. This commentary looks more closely at the implications of emphasizing the practical and political dimensions of open government data. It argues that researchers should explicate how and in what way open government data policies present solutions to what kind of problems. Such explications should be based on a detailed empirical analysis of how different actors do or do not do open data. The key question to be continuously asked and answered when studying and implementing open government data is how the solutions openness present latch onto the problem they aim to solve.

Deferred prosecution agreements (DPAs) are a legal tool for the nontrial resolution of cases of corruption. Each DPA is accompanied by a Statement of Facts that provides detailed and publicly available textual records of the given cases, including summarized evidence of who was involved, what they committed, and with whom. These statements can be translated into networks amenable to social network analysis allowing an analysis of the structure and dynamics of each case. In this study, we show how to extract information about which actors were involved in a given case, the relations and interactions among these actors (e.g., communication or payments), and their relevant individual attributes (gender, affiliation, and sector) from five Statements of Fact. We code the extracted information manually with two independent coders and subsequently, we assess the inter-coder reliability. For assessing the coding reliability of nodes and attributes, we use a matching coefficient, whereas for assessing the coding reliability of ties, we construct a network from the coding of each coder and subsequently calculate the graph correlations of the two resulting networks. The coding of nodes and ties in the five extracted networks turns out to be highly reliable with only slightly lower coding reliability in the case of the largest network. The coding of attributes is highly reliable as well, although it is prone to missing data on actors’ gender. We conclude by discussing the flexibility of our data collection framework and its extension by including network dynamics and nonhuman actors (such as companies) in the network representation.

Explainability is highly desired in machine learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. While the field of explainable ML has expanded in recent years, much of this work has not taken real-world needs into account. A majority of proposed methods are designed with generic explainability goals without well-defined use cases or intended end users and evaluated on simplified tasks, benchmark problems/datasets, or with proxy users (e.g., Amazon Mechanical Turk). We argue that these simplified evaluation settings do not capture the nuances and complexities of real-world applications. As a result, the applicability and effectiveness of this large body of theoretical and methodological work in real-world applications are unclear. In this work, we take steps toward addressing this gap for the domain of public policy. First, we identify the primary use cases of explainable ML within public policy problems. For each use case, we define the end users of explanations and the specific goals the explanations have to fulfill. Finally, we map existing work in explainable ML to these use cases, identify gaps in established capabilities, and propose research directions to fill those gaps to have a practical societal impact through ML. The contribution is (a) a methodology for explainable ML researchers to identify use cases and develop methods targeted at them and (b) using that methodology for the domain of public policy and giving an example for the researchers on developing explainable ML methods that result in real-world impact.

Today, technological developments are ever-growing yet fragmented. Alongside inconsistent digital approaches and attitudes across city administrations, such developments have made it difficult to reap the benefits of city digital twins. Bringing together experiences from five research projects, this paper discusses these digital twins based on two digital integration methodologies—systems and semantic integration. We revisit the nature of the underlying technologies, and their implications for interoperability and compatibility in the context of planning processes and smart urbanism. Semantic approaches present a new opportunity for bidirectional data flows that can inform both governance processes and technological systems to co-create, cross-pollinate, and support optimal outcomes. Building on this opportunity, we suggest that considering the technological dimension as a new addition to the trifecta of economic, environmental, and social sustainability goals that guide planning processes, can aid governments to address this conundrum of fragmentation, interoperability, and compatibility.

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.

There is an increasing gap between the policy cycle’s speed and that of technological and social change. This gap is becoming broader and more prominent in robotics, that is, movable machines that perform tasks either automatically or with a degree of autonomy. This is because current legislation was unprepared for machine learning and autonomous agents. As a result, the law often lags behind and does not adequately frame robot technologies. This state of affairs inevitably increases legal uncertainty. It is unclear what regulatory frameworks developers have to follow to comply, often resulting in technology that does not perform well in the wild, is unsafe, and can exacerbate biases and lead to discrimination. This paper explores these issues and considers the background, key findings, and lessons learned of the LIAISON project, which stands for “Liaising robot development and policymaking,” and aims to ideate an alignment model for robots’ legal appraisal channeling robot policy development from a hybrid top-down/bottom-up perspective to solve this mismatch. As such, LIAISON seeks to uncover to what extent compliance tools could be used as data generators for robot policy purposes to unravel an optimal regulatory framing for existing and emerging robot technologies.

Donor organizations and multilaterals require ways to measure progress toward the goals of creating an open internet, and condition assistance on recipient governments maintaining access to information online. Because the internet is increasingly becoming a leading tool for exchanging information, authoritarian governments around the world often seek methods to restrict citizens’ access. Two of the most common methods for restricting the internet are shutting down internet access entirely and filtering specific content. We conduct a systematic literature review of articles on the measurement of internet censorship and find that little work has been done comparing the tradeoffs of using different methods to measure censorship on a global scale. We compare the tradeoffs between measuring these phenomena using expert analysis (as measured by Freedom House and V-Dem) and remote measurement with manual oversight (as measured by Access Now and the OpenNet Initiative [ONI]) for donor organizations that want to incentivize and measure good internet governance. We find that remote measurement with manual oversight is less likely to include false positives, and therefore may be more preferable for donor organizations that value verifiability. We also find that expert analysis is less likely to include false negatives, particularly for very repressive regimes in the Middle East and Central Asia and therefore these data may be preferable for advocacy organizations that want to ensure very repressive regimes are not able to avoid accountability, or organizations working primarily in these areas.

The past decade has seen the rise of “data portals” as online devices for making data public. They have been accorded a prominent status in political speeches, policy documents, and official communications as sites of innovation, transparency, accountability, and participation. Drawing on research on data portals around the world, data portal software, and associated infrastructures, this paper explores three approaches for studying the social life of data portals as technopolitical devices: (a) interface analysis, (b) software analysis, and (c) metadata analysis. These three approaches contribute to the study of the social lives of data portals as dynamic, heterogeneous, and contested sites of public sector datafication. They are intended to contribute to critically assessing how participation around public sector datafication is invited and organized with portals, as well as to rethinking and recomposing them.

Innovative, responsible data use is a critical need in the global response to the coronavirus disease-2019 (COVID-19) pandemic. Yet potentially impactful data are often unavailable to those who could utilize it, particularly in data-poor settings, posing a serious barrier to effective pandemic mitigation. Data challenges, a public call-to-action for innovative data use projects, can identify and address these specific barriers. To understand gaps and progress relevant to effective data use in this context, this study thematically analyses three sets of qualitative data focused on/based in low/middle-income countries: (a) a survey of innovators responding to a data challenge, (b) a survey of organizers of data challenges, and (c) a focus group discussion with professionals using COVID-19 data for evidence-based decision-making. Data quality and accessibility and human resources/institutional capacity were frequently reported limitations to effective data use among innovators. New fit-for-purpose tools and the expansion of partnerships were the most frequently noted areas of progress. Discussion participants identified building capacity for external/national actors to understand the needs of local communities can address a lack of partnerships while de-siloing information. A synthesis of themes demonstrated that gaps, progress, and needs commonly identified by these groups are relevant beyond COVID-19, highlighting the importance of a healthy data ecosystem to address emerging threats. This is supported by data holders prioritizing the availability and accessibility of their data without causing harm; funders and policymakers committed to integrating innovations with existing physical, data, and policy infrastructure; and innovators designing sustainable, multi-use solutions based on principles of good data governance.

Increasingly, laws are being proposed and passed by governments around the world to regulate artificial intelligence (AI) systems implemented into the public and private sectors. Many of these regulations address the transparency of AI systems, and related citizen-aware issues like allowing individuals to have the right to an explanation about how an AI system makes a decision that impacts them. Yet, almost all AI governance documents to date have a significant drawback: they have focused on what to do (or what not to do) with respect to making AI systems transparent, but have left the brunt of the work to technologists to figure out how to build transparent systems. We fill this gap by proposing a stakeholder-first approach that assists technologists in designing transparent, regulatory-compliant systems. We also describe a real-world case study that illustrates how this approach can be used in practice.

One of the drivers for pushing for open data as a form of corruption control stems from the belief that in making government operations more transparent, it would be possible to hold public officials accountable for how public resources are spent. These large datasets would then be open to the public for scrutiny and analysis, resulting in lower levels of corruption. Though data quality has been largely studied and many advancements have been made, it has not been extensively applied to open data, with some aspects of data quality receiving more attention than others. One key aspect however—accuracy—seems to have been overlooked. This gap resulted in our inquiry: how is accurate open data produced and how might breakdowns in this process introduce opportunities for corruption? We study a government agency situated within the Brazilian Federal Government in order to understand in what ways is accuracy compromised. Adopting a distributed cognition (DCog) theoretical framework, we found that the production of open data is not a neutral activity, instead it is a distributed process performed by individuals and artifacts. This distributed cognitive process creates opportunities for data to be concealed and misrepresented. Two models mapping data production were generated, the combination of which provided an insight into how cognitive processes are distributed, how data flow, are transformed, stored, and processed, and what instances provide opportunities for data inaccuracies and misrepresentations to occur. The results obtained have the potential to aid policymakers in improving data accuracy.

A proliferation of data-generating devices, sensors, and applications has led to unprecedented amounts of digital data. We live in an era of datafication, one in which life is increasingly quantified and transformed into intelligence for private or public benefit. When used responsibly, this offers new opportunities for public good. The potential of data is evident in the possibilities offered by open data and data collaboratives—both instances of how wider access to data can lead to positive and often dramatic social transformation. However, three key forms of asymmetry currently limit this potential, especially for already vulnerable and marginalized groups: data asymmetries, information asymmetries, and agency asymmetries. These asymmetries limit human potential, both in a practical and psychological sense, leading to feelings of disempowerment and eroding public trust in technology. Existing methods to limit asymmetries (such as open data or consent) as well as some alternatives under consideration (data ownership, collective ownership, personal information management systems) have limitations to adequately address the challenges at hand. A new principle and practice of digital self-determination (DSD) is therefore required. The study and practice of DSD remain in its infancy. The characteristics we have outlined here are only exploratory, and much work remains to be done so as to better understand what works and what does not. We suggest the need for a new research framework or agenda to explore DSD and how it can address the asymmetries, imbalances, and inequalities—both in data and society more generally—that are emerging as key public policy challenges of our era.

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.

In 2014, Nigeria halted transmission of wild poliovirus for the first time in its history. A critical enabling component in this historic achievement was the use of satellite data to produce more accurate maps and population estimates used in planning and implementing vaccination campaigns. This article employs a value-of-information approach to estimate the net socioeconomic benefits associated with this use of satellite data. We calculate the increase in the likelihood of halting transmission of polio associated with the use of satellite-based information compared to traditional data sources, and we consider the benefits associated with savings to the healthcare system as well as health benefits. Using a conservative approach focused on just 1 year of benefits, we estimate net socioeconomic benefits of between $46.0 million and $153.9 million. In addition to these quantified benefits, we also recognize qualitative benefits associated with improving human health, reaching marginalized communities, and building capacity among local populations. We also explore the substantial benefits associated with follow-on projects that have made use of the satellite-based data products and methodologies originally developed for the Nigeria polio eradication effort.

The exponential growth of data collection opens possibilities for analyzing data to address political and societal challenges. Still, European cities are not utilizing the potential of data generated by its citizens, industries, academia, and public authorities for their public service mission. The reasons are complex and relate to an intertwined set of organizational, technological, and legal barriers, although good practices exist that could be scaled, sustained, and further developed. The article contributes to research on data-driven innovation in the public sector comparing high-level expectations on data ecosystems with actual practices of data sharing and innovation at the local and regional level. Our approach consists in triangulating the analysis of in-depth interviews with representatives of the local administrations with documents obtained from the cities. The interviews investigated the experiences and perspectives of local administrations regarding establishing a local or regional data ecosystem. The article examines experiences and obstacles to data sharing within seven administrations investigating what currently prevents the establishment of data ecosystems. The findings are summarized along three main lines. First, the limited involvement of private sector organizations as actors in local data ecosystems through emerging forms of data sharing became evident. Second, we observed the concern over technological aspects and the lack of attention on social or organizational issues. Third, a conceptual decision to apply a centralized and not a federated digital infrastructure is noteworthy.

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 commentary explores the potential of private companies to advance scientific progress and solve social challenges through opening and sharing their data. Open data can accelerate scientific discoveries, foster collaboration, and promote long-term business success. However, concerns regarding data privacy and security can hinder data sharing. Companies have options to mitigate the challenges through developing data governance mechanisms, collaborating with stakeholders, communicating the benefits, and creating incentives for data sharing, among others. Ultimately, open data has immense potential to drive positive social impact and business value, and companies can explore solutions for their specific circumstances and tailor them to their specific needs.

Data sharing is a requisite for developing data-driven innovation and collaboration at the local scale. This paper aims to identify key lessons and recommendations for building trustworthy data governance at the local scale, including the public and private sectors. Our research is based on the experience gained in Rennes Metropole since 2010 and focuses on two thematic use cases: culture and energy. For each one, we analyzed how the power relations between actors and the local public authority shape the modalities of data sharing and exploitation. The paper will elaborate on challenges and opportunities at the local level, in perspective with the national and European frameworks.