Governments around the world responded at roughly the same time but in several different ways to the emerging threat of COVID-19 in early 2020. This article sets out the nature of the different strategies that emerged over the course of the pandemic, focussing on the policy tools deployed. Some of these efforts were successful in containing the coronavirus while others were not, in some cases due to poor initial choices and in others due to poor implementation of the chosen strategy. Although the initial understanding each government had of the nature of the disease was the same, different state capacities and different levels of preparedness and effective leadership can be seen to have resulted over time in the emergence of six distinct approaches to the pandemic which, once deployed, proved difficult, although not impossible, to change as the pandemic unfolded.

Despite the highest ranks on pandemic preparedness assessments European welfare states encounter great difficulty in responding effectively to the COVID-19 outbreak. In this article we compare the governance of COVID-19 response in 48 Eurasian countries and a selection of European and SARS (2003) exposed Asian countries during the first wave of the COVID-19 outbreak until 1 June 2020, using data from the COVID-19 Health System Response Monitor and the Oxford COVID-19 Government Response Tracker, recent scientific literature and policy documents.
Pandemic preparedness during the first wave of COVID-19 evolved from specialist infectious disease control to a broad governance of population mitigation, which in at least half of Eurasian countries lacked appropriate authority and capacity. In the directly operational response in Asian countries, preparedness encompasses a whole of government approach, an engaged and active community and private actors. Preparedness requires and reflects both vertical and horizontal coordination as well as policies that fit with the political economy of a country and region.

In June 2020, the Flemish populist radical right party Vlaams Belang (VB) published the Corona Blunder Book (CBB; Coronablunderboek in Dutch), detailing the government’s mistakes in handling the COVID-19 crisis. Populist parties can ‘perform’ crisis by emphasising the mistakes made by opponents (Moffitt, 2015) and may use a specifically populist discursive style, consisting largely of aggressive and sarcastic language (Brubaker, 2017). This paper takes the CBB as a case study in the populist performance of crisis and the populist style, finding that the book is, first, a clear example of populist ‘everyman’ stylistics and the performance of crisis, and, second, that VB uses the book to shift the COVID-19 crisis from a public health crisis to a crisis of governance, seeking to blame Belgium’s federal structure for the government’s alleged mismanagement of the COVID-19 pandemic and hence arguing for Flemish independence, one of the party’s main agenda points.

The Dutch digital fraud detection system SyRI was announced to set up to detect social security fraud quickly and effectively and by doing so, maintain support for the social security system. It was the formal position that for the sake of effectiveness, no information about the algorithm and very limited information about the application of the system should be shared. The authors argue on the basis of a policy analysis, a legal exploration and a literature study that the lack of transparency about the chosen method and the application of the digital fraud detection system in social security can have far-reaching consequences for both the individual and society . The information sharing and the use of algorithms can lead to suspicion of and declining confidence in the government, and a reduced motivation to comply with the prevailing rules. This could undermine the original purpose.

Algorithms are starting to play an increasingly prominent role in government organizations. The argument is that algorithms can make more objective and efficient decisions than humans. At the same time, recent scandals have highlighted that there are still many problems connected to algorithms in the public sector. There is an increasing emphasis on ethical requirements for algorithms and we aim to connect these requirements to insights from public administration on the use of technologies in the public sector. We stress the need for responsible algorithmization – responsible organizational practices around the use of algorithms – and argue that this is needed to maintain the trust of citizens. We present two key components of responsible algorithmization – value-sensitivity and transparency – and show how these components connect to algorithmization and can contribute to citizen trust. We end the article with an agenda for research into the relation between responsible algorithmization and trust.

Algorithms applied in public administration are often criticized for lack of transparency. Lawmakers and citizens alike expect that automated decisions based on algorithmic recommendations to be explainable. The focus of this article is the organizational context behind the idea of transparent algorithms. Transparency is portrayed as one of numerous values that are at play when algorithms are applied in public administration. The article shows that applying algorithms may lead to conflicts between these values. Such conflicts often result in trade-off decisions. Looking from the organizational perspective, we describe how such trade-offs can be made both explicitly and implicitly. The article thus shows the complexity of algorithmic trade-offs. As a result of this complexity, we not only call for more transparency about algorithms, but also more transparency about trade-offs that take place in public administration. Finally, we present a research agenda focused on studying the organization of trade-offs.

This article contains a critical reflection on eXplainable Artificial Intelligence (XAI): the idea that AI based decision-making using AI should be transparent to people faced with these decisions. We discuss the main objections to XAI. XAI focuses on a variety of explainees, with different expectations and values; XAI is not a neutral activity, but very value-sensitive; AI is dynamic and so XAI quickly becomes obsolete; many problems are ‘wicked’, which further complicates XAI. In addition, the context of XAI matters – a high level of politicization and a high perceived impact of AI-based decisions, will often result in much criticism of AI and will limit the opportunities of XAI. We also discuss a number of alternative or additional strategies – more attention to negotiated algorithms; to competing algorithms; or to value-sensitive algorithms, which may contribute to more trust in AI-based decision-making.

The police use all sorts of information to fulfil their tasks. Whereas collection and interpretation of information traditionally could only be done by humans, the emergence of ‘Big Data’ creates new opportunities and dilemmas. On the one hand, large amounts of data can be used to train algorithms. This allows them to ‘predict’ offenses such as bicycle theft, burglary, or even serious crimes such as murder and terrorist attacks. On the other hand, highly relevant questions on purpose, effectiveness, and legitimacy of the application of machine learning/‘artificial intelligence’ drown all too often in the ocean of Big Data. This is particularly problematic if such systems are used in the public sector in democracies, where the rule of law applies, and where accountability, as well as the possibility for judicial review, are guaranteed. In this article, we explore the role transparency could play in reconciling these opportunities and dilemmas. While some propose making the systems and data they use themselves transparent, we submit that an open and broad discussion on purpose and objectives should be held during the design process. This might be a more effective way of embedding ethical and legal principles in the technology, and of ensuring legitimacy during application.

Recently, studies have burgeoned on the link between populism and demands for democratic reforms. In particular, scholars have been debating the link between populist citizens or voters and support for referendums. In this article, we examine voters of populist parties (Vlaams Belang (VB) and Parti du Travail de Belgique-Partij van de Arbeid (PTB-PVDA)) in Belgium in 2019 and we look at their attitudes towards various types of democratic reforms. We find that voters of populist parties differ from the non-populist electorate in their support for different kinds of reforms of representative democracy. Voters of VB and PTB-PVDA have in common stronger demands for limiting politicians’ prerogatives, for introducing binding referendums and for participatory budgeting. While Vlaams Belang voters are not significantly different from the non-populist electorate on advisory referendums, citizens’ forums or technocratic reform, PVDA-PTB voters seem more enthusiastic.

As a response to the perceived legitimacy crisis that threatens modern democracies, local government has increasingly become a laboratory for democratic renewal and citizen participation. This article studies whether and why local party chairs support democratic innovations fostering more citizen participation. More specifically, we analyse the relative weight of ideas, interests and institutions in explaining their support for citizen-centred democracy. Based on the Belgian Local Chairs Survey in 2018 (albeit restricting our analysis to Flanders), the central finding is that ideas matter more than interests and institutions. Ideology is alive and kicking with regard to democratic innovation, with socialist and ecologist parties and populist parties being most supportive of participatory arrangements. By contrast, interests and institutions play, at this stage, a minor role in explaining support for participatory innovations.

Predictive tools as instruments for understanding and responding to risky behaviour as early as possible are increasingly becoming a normal feature in local and state agencies. A risk that arises from the implementation of these predictive tools is the problem of dirty data. The input of incorrect or illegally obtained information (‘dirty data’) can influence the quality of the predictions used by local and state agencies, such as the police. The article focuses on the risks of dirty data in predictive policing by the Dutch Police. It describes the possibilities to prevent dirty data from being used in predictive policing tools, such as the Criminality Anticipation System (CAS). It concludes by emphasizing the importance of transparency for any serious solution looking to eliminate the use of dirty data in predictive policing.

Technological advancements in the field of data analytics, algorithms and AI have dramatically increased opportunities for workplace monitoring. In this article, we discuss some of these digital technologies, and examine their impact on employment relationships and the quality of work. Based on desk research, literature review and interviews, the Rathenau Institute examined a wide range of digital instruments, their scientific basis, implications for the quality of work and relevant legal frameworks. Digital monitoring technologies often quantify work activities. We argue that it is important for organisations to realise that such quantification often negatively impacts both job quality and employment relationships. Responsible use of digital monitoring tools not only requires a broad societal and political dialogue about privacy, discrimination and workload but also a critical reflection on the aim of organisations to use data to understand workers, while not everything of value can be captured in data.

In this article, we propose an in-depth exploration of blank and null ballots in the recent 2018 local elections in Wallonia (Belgium). In the official results, both blank and null ballots are merged together and are classified as invalid votes. After obtaining the authorization to access genuine electoral ballots, we study the votes which were not considered for the composition of local councils in detail. The dataset is a representative sample of 13,243 invalidated ballots from 49 Walloon municipalities. We first describe how many of these invalidated ballots are blank and how many are null votes, as well as the nature of the nulled votes (unintended errors or intentionally spoiled ballots). Second, we dig deeper into the differences between ballots that have been intentionally invalidated by voters (blank votes and intentional null votes) and ballots non-intentionally invalidated. Our results show that most of the ballots (two-thirds) are null ballots and that among them, half are unintentional null ballots. Finally, we show that contextual (socio-demographic and political) factors explain the variations in intentional and unintentional null votes across municipalities.

Citizens and scholars excel in identifying and analysing government failure. It is important indeed to understand public sector, but while governments in the Low Countries are doing well on average and very well from an internationally perspective, most attention is focused on the errors and mishaps. This article argues for a robust positive perspective on the public sector as a complement to existing research. From a scientific perspective, public administration must set itself the ambition to connect and aggregate existing positive insights even better. From a social perspective, the discipline must prevent the gap from being filled by a wholesale rejection of democratic government or the use of unproven miracle cures. This article elaborates the starting points for Positive Public Administration, arguing that this perspective should be comprehensive, context related, inter-subjective, learning-oriented, and robustly scientific in nature. The article also introduces the other contributions in this special issue, which together give an initial interpretation of positive public administration.

Fiscal consolidation confronts federal states with a collective action problem, especially in federations with a tightly coupled fiscal regime such as Belgium. However, the Belgian federation has successfully solved this collective action problem even though it lacks the political institutions that the literature on dynamic federalism has identified as the main mechanisms through which federal states achieve cooperation across levels of government. This article argues that the regionalization of the party system, on the one hand, and the rationalization of the deficit problem by the High Council of Finance, on the other, are crucial to understand how Belgium was able to solve the collective action problem despite its tightly coupled fiscal regime and particularly high levels of deficits and debts. The article thus emphasizes the importance of compromise and consensus in reducing deficits and debts in federal states.

This article presents the research program into governance in a knowledge society of professor Albert Meijer and colleagues at Utrecht University. The knowledge society is a society in which (1) citizens are higher educated that ever before and their level of education largely determines their societal position, (2) knowledge plays a key role in administrative and policy processes and is increasingly contested and (3) technology plays a key role in every facet of societal life. Research into governance of and in the knowledge society requires a transdisciplinary and plural approach to scientific work. Transdisciplinarity entails combining insights from science with various forms of contextual and practical knowledge. A plural approach to scientific works means that we should not only do explanatory empirical work but also theoretical, normative and prescriptive research. The overall ambition of this research program is to contribute to a democratic debate about the governance of the future.