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.

Data and data science offer tremendous potential to address some of our most intractable public problems (including the Covid-19 pandemic). At the same time, recent years have shown some of the risks of existing and emerging technologies. An updated framework is required to balance potential and risk, and to ensure that data is used responsibly. Data responsibility is not itself a new concept. However, amid a rapidly changing technology landscape, it has become increasingly clear that the concept may need updating, in order to keep up with new trends such as big data, open data, the Internet of things, and artificial intelligence, and machine learning. This paper seeks to outline 10 approaches and innovations for data responsibility in the 21st century. The 10 emerging concepts we have identified include:

End-to-end data responsibility

Decision provenance

Professionalizing data stewardship

From data science to question science

Contextual consent

Responsibility by design

Data asymmetries and data collaboratives

Personally identifiable inference

Group privacy

Data assemblies

Each of these is described at greater length in the paper, and illustrated with examples from around the world. Put together, they add up to a framework or outline for policy makers, scholars, and activists who seek to harness the potential of data to solve complex social problems and advance the public good. Needless to say, the 10 approaches outlined here represent just a start. We envision this paper more as an exercise in agenda-setting than a comprehensive survey.

This article explores the role of interoperability in the development of digital public services in Europe, analyzing the effects of an European Union (EU)-level initiative (the European interoperability framework, EIF) and the development of e-Government services on how citizens interact online with public administrations. The EIF is a common EU framework providing guidance on public sector interoperability. EU countries are not mandated to follow the EIF, but they are encouraged to take up its guidance in their respective national interoperability frameworks (NIFs). Against this background, this article tests two hypotheses: (a) the introduction of NIFs facilitates the online interaction between citizens and public administrations and (b) better e-Government services encourage citizens to interact online with public administrations. Both hypotheses are confirmed by a panel data analysis covering 26 European countries over the period 2012–2019. The analysis relies on a dummy variable reflecting the adoption of NIFs, built by carefully examining official documents of the countries in the scope of the analysis. Based on the empirical results, this article puts forward two main policy recommendations. First, efforts to improve e-Government services across Europe should be intensified in order to support the overarching digital agenda of the EU and increase benefits for European citizens. Second, interoperability should become a central element when designing new digital public services. Therefore, the European Commission could foster a common approach to interoperability of digital public services across the EU by strengthening the governance of interoperability initiatives and encouraging the adoption of specific interoperability requirements.

Internet of Things (IoT) devices such as connected sensors are increasingly being used in the public sector, often deployed and collecting data in public spaces. A theme commonly seen in the rhetoric surrounding public space IoT initiatives is empowerment, and these deployments are broadly perceived as beneficial by policy makers. However, such technology presents new governance challenges. It is important to ask who is empowered and who benefits, and we must ensure that such technological interventions follow democratic principles and are trusted by citizens. In this paper, we investigate how risk, transparency, and data governance require careful consideration in this domain, describing work which investigates how these combine to form components of trusted IoT ecosystems. This includes an overview of the landscape of public space IoT deployments, consideration of how they may often be subsumed in idealized smart city focused rhetoric, and discussion of how methodologies such as design fiction in community settings can uncover potential risks and concerns. Our findings suggest that agency, value and intent associated with IoT systems are key components that must be made transparent, particularly when multiple actors and stakeholders are involved. We suggest that good governance requires consideration of these systems in their entirety, throughout the full planning, implementation, and evaluation process, and in consultation with multiple stakeholders who are impacted, including the public. To achieve this effectively, we argue for transparency at the device and system level, which may require legislative change.

We present PolicyCLOUD: a prototype for an extensible serverless cloud-based system that supports evidence-based elaboration and analysis of policies. PolicyCLOUD allows flexible exploitation and management of policy-relevant dataflows, by enabling the practitioner to register datasets and specify a sequence of transformations and/or information extraction through registered ingest functions. Once a possibly transformed dataset has been ingested, additional insights can be retrieved by further applying registered analytic functions to it. PolicyCLOUD was built as an extensible framework toward the creation of an analytic ecosystem. As of now, we have developed several essential ingest and analytic functions that are built-in within the framework. They include data cleaning, enhanced interoperability, and sentiment analysis generic functions; in addition, a trend analysis function is being created as a new built-in function. PolicyCLOUD has also the ability to tap on the analytic capabilities of external tools; we demonstrate this with a social dynamics tool implemented in conjunction with PolicyCLOUD, and describe how this stand-alone tool can be integrated with the PolicyCLOUD platform to enrich it with policy modeling, design and simulation capabilities. Furthermore, PolicyCLOUD is supported by a tailor-made legal and ethical framework derived from privacy/data protection best practices and existing standards at the EU level, which regulates the usage and dissemination of datasets and analytic functions throughout its policy-relevant dataflows. The article describes and evaluates the application of PolicyCLOUD to four families of pilots that cover a wide range of policy scenarios.

Evaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number $ R(t) $ , a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of $ R(t) $ could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.

Algorithmic decision tools (ADTs) are being introduced into public sector organizations to support more accurate and consistent decision-making. Whether they succeed turns, in large part, on how administrators use these tools. This is one of the first empirical studies to explore how ADTs are being used by Street Level Bureaucrats (SLBs). The author develops an original conceptual framework and uses in-depth interviews to explore whether SLBs are ignoring ADTs (algorithm aversion); deferring to ADTs (automation bias); or using ADTs together with their own judgment (an approach the author calls “artificing”). Interviews reveal that artificing is the most common use-type, followed by aversion, while deference is rare. Five conditions appear to influence how practitioners use ADTs: (a) understanding of the tool (b) perception of human judgment (c) seeing value in the tool (d) being offered opportunities to modify the tool (e) alignment of tool with expectations.

COVID-19 has impacted all aspects of everyday normalcy globally. During the height of the pandemic, people shared their (PI) with one goal—to protect themselves from contracting an “unknown and rapidly mutating” virus. The technologies (from applications based on mobile devices to online platforms) collect (with or without informed consent) large amounts of PI including location, travel, and personal health information. These were deployed to monitor, track, and control the spread of the virus. However, many of these measures encouraged the trade-off on privacy for safety. In this paper, we reexamine the nature of privacy through the lens of safety focused on the health sector, digital security, and what constitutes an infraction or otherwise of the privacy rights of individuals in a pandemic as experienced in the past 18 months. This paper makes a case for maintaining a balance between the benefit, which the contact tracing apps offer in the containment of COVID-19 with the need to ensure end-user privacy and data security. Specifically, it strengthens the case for designing with transparency and accountability measures and safeguards in place as critical to protecting the privacy and digital security of users—in the use, collection, and retention of user data. We recommend oversight measures to ensure compliance with the principles of lawful processing, knowing that these, among others, would ensure the integration of privacy by design principles even in unforeseen crises like an ongoing pandemic; entrench public trust and acceptance, and protect the digital security of people.

The COVID-19 pandemic and associated measures implemented have rapidly changed how people move about and behave in society. Utilizing data on people’s mobility could provide unique and valuable insights to governments and institutions to better manage the crisis. These entities, however, have not traditionally had access to, nor the experience of applying, continuous anonymized and aggregated data on people mobility. This article aims to show how the Public Health Agency in Sweden successfully collaborated with a Nordic Telecoms operator to make use of such data during the COVID-19 pandemic. Specifically, it investigates how the collaboration started, approaches used to go from data to insight, outcomes and impact, and lessons learned on both sides. Telia, the largest telecom operator in the Nordics, had an existing product commercially available that provided anonymized and aggregated insights about people’s movement. Several challenges existed within Telia as it was the first time worldwide a collaboration with a Public Health Agency would take place and social benefits had to be weighed against commercial and reputational risks. The hypothesis at the beginning of the pandemic was that the solution could be adapted to fit the needs of policymakers and the internal challenges could be overcome, while providing a meaningful contribution to the fight against the virus. The results show that it is possible to both form a mutually beneficial collaboration between a telecom operator and a public institution, and to make use of mobility data in evidence-based policymaking without compromising applicable personal data protection laws.

This study investigates the data protection concerns arising in the context of the cross-border interoperability of Electronic Health Record (EHR) systems in the European Union. The article first introduces the policies on digital health and examines the related interoperability issues. Second, the work analyses the latest Recommendation of the European Commission on this topic. Then, the study discusses the rules and the obligations settled by the General Data Protection Regulation to be taken into account when developing interoperable EHRs. According to the data protection by design and by default provision, EHR systems should be designed ex ante to guarantee data protection rules.

We build a computational framework to support the planning of development and the evaluation of budgetary strategies toward the 2030 Agenda. The methodology takes into account some of the complexities of the political economy underpinning the policymaking process: the multidimensionality of development, the interlinkages between these dimensions, and the inefficiencies of policy interventions, as well as institutional factors that promote or discourage these inefficiencies. The framework is scalable and usable even with limited publicly available information: development-indicator data. However, it can be further refined as more data becomes available, for example, on public expenditure. We demonstrate its usage through an application for the Mexican federal government. For this, we infer historical policy priorities, that is, the non-observable allocations of transformative resources that generated past changes in development indicators. We also show how to use the tool to assess the feasibility of development goals, to measure policy coherence, and to identify accelerators. Overall, the framework and its computational tools allow policymakers and other stakeholders to embrace a complexity (and a quantitative) view to tackle the challenges of the Sustainable Development Goals.

Anonymous and aggregated statistics derived from mobile phone data have proven efficacy as a proxy for human mobility in international development work and as inputs to epidemiological modeling of the spread of infectious diseases such as COVID-19. Despite the widely accepted promise of such data for better development outcomes, challenges persist in their systematic use across countries. This is not only the case for steady-state development use cases such as in the transport or urban development sectors, but also for sudden-onset emergencies such as epidemics in the health sector or natural disasters in the environment sector. This article documents an effort to gain systematized access to and use of anonymized, aggregated mobile phone data across 41 countries, leading to fruitful collaborations in nine developing countries over the course of one year. The research identifies recurring roadblocks and replicable successes, offers lessons learned, and calls for a bold vision for future successes. An emerging model for a future that enables steady-state access to insights derived from mobile big data - such that they are available over time for development use cases - will require investments in coalition building across multiple stakeholders, including local researchers and organizations, awareness raising of various key players, demand generation and capacity building, creation and adoption of standards to facilitate access to data and their ethical use, an enabling regulatory environment and long-term financing schemes to fund these activities.

Data have played a role in urban mobility policy planning for decades, especially in forecasting demand, but much less in policy evaluations and assessments. The surge in availability and openness of (big) data in the last decade seems to provide new opportunities to meet demand for evidence-based policymaking. This paper reviews how different types of data are employed in assessments published in academic journals by analyzing 74 cases. Our review finds that (a) academic literature has currently provided limited insight in new data developments in policy practice; (b) research shows that the new types of big data provide new opportunities for evidence-based policy-making; however, (c) they cannot replace traditional data usage (surveys and statistics). Instead, combining big data with survey and Geographic Information System data in ex-ante assessments, as well as in developing decision support tools, is found to be the most effective. This could help policymakers not only to get much more insight from policy assessments, but also to help avoid the limitations of one certain type of data. Finally, current research projects are rather data supply-driven. Future research should engage with policy practitioners to reveal best practices, constraints, and potential of more demand-driven data use in mobility policy assessments in practice.

Independent data stewardship remains a core component of good data governance practice. Yet, there is a need for more robust independent data stewardship models that are able to oversee data-driven, multi-party data sharing, usage and re-usage, which can better incorporate citizen representation, especially in relation to personal data. We propose that data foundations—inspired by Channel Islands’ foundations laws—provide a workable model for good data governance not only in the Channel Islands, but also elsewhere. A key advantage of this model—in addition to leveraging existing legislation and building on established precedent—is the statutory role of the guardian that is a unique requirement in the Channel Islands, and when interpreted in a data governance model provides the independent data steward. The principal purpose for this paper, therefore, is to demonstrate why data foundations are well suited to the needs of data sharing initiatives. We further examine how data foundations could be established in practice—and provide key design principles that should be used to guide the design and development of any data foundation.

Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

The COVID-19 pandemic has accelerated the use of mobile operator data to support public policy, although without a universal governance framework for its application. This article describes five principles to guide and assist statistical agencies, mobile network operators and intermediary service providers, who are actively working on projects using mobile operator data to support governments in monitoring the effectiveness of its COVID-19 related interventions. These are principles of necessity and proportionality, of professional independence, of privacy protection, of commitment to quality, and of international comparability. Compliance with each of these principles can help maintain public trust in the handling of these sensitive data and their results, and therefore keep citizen support for government policies. Three projects (in Estonia, Ghana, and the Gambia) were described and reviewed with respect to the compliance and applicability of the five principles. Most attention was placed on privacy protection, somewhat at the expense of the quality of the compiled indicators. The necessity and proportionality in the choice of mobile operator data can be very well justified given the need for timely, frequent and granular indicators. Explicitly addressing the five principles in the preparation of a project should give confidence to the statistical agency and its partners, that enough care has been exercised in the set up and implementation of the project, and should convey trust to public and government in the use mobile operator data for policy purposes.

We outline a theory of algorithmic attention rents in digital aggregator platforms. We explore the way that as platforms grow, they become increasingly capable of extracting rents from a variety of actors in their ecosystems—users, suppliers, and advertisers—through their algorithmic control over user attention. We focus our analysis on advertising business models, in which attention harvested from users is monetized by reselling the attention to suppliers or other advertisers, though we believe the theory has relevance to other online business models as well. We argue that regulations should mandate the disclosure of the operating metrics that platforms use to allocate user attention and shape the “free” side of their marketplace, as well as details on how that attention is monetized.

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.

Digital identity (eID) systems are a crucial piece in the digital services ecosystem. They connect individuals to a variety of socioeconomic opportunities but can also reinforce power asymmetries between organizations and individuals. Data collection practices can negatively impact an individual’s right to privacy, autonomy, and self-determination. Protecting individual rights, however, may be at odds with imperatives of profit maximization or national security. The use of eID technologies is hence highly contested. Current approaches to governing eID systems have been unable to fully address the trade-offs between the opportunities and risks associated with these systems. The responsible innovation (RI) literature provides a set of principles to govern disruptive innovations, such as eID systems, toward societally desirable outcomes. This article uses RI principles to develop a framework to govern eID systems in a more inclusive, responsible, and user-centered manner. The proposed framework seeks to complement existing practices for eID system governance by bringing forth principles of deliberation and democratic engagement to build trust amongst stakeholders of the eID system and deliver shared socioeconomic benefits.