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The study of policy tools has been undertaken for several decades. It has isolated and examined many different types of tools utilized by governments over the course of history and examined in detail how they are arranged into policy mixes or portfolios of tools. Recent developments in society and technology, however, have brought to the fore the possibility of using new or previously little-used tools such as platforms, co-production, nudges, as well as data-driven techniques, such as big data and artificial intelligence. These are added to the toolbox governments have at their disposal when designing policy responses to both new and old problems. Like any other tool, however, each of these has its strengths and weaknesses. This paper addresses the promises and pitfalls of these new tools, and assesses the degree to which their deployment and effectiveness can be understood using the kinds of typologies and concepts developed to deal with more traditional policy instruments.

AbstractThe recent surge of data-driven methods in social policy have created new opportunities to assess existing poverty programs. The expectation is that the combination of advanced methods and more data can calculate the effectiveness of public interventions more accurately and tailor local initiatives accordingly. Specifically, nonmonetary indicators are increasingly being measured at micro levels in order to target social exclusion in combination with poverty. However, the multidimensional character of poverty, local context, and data matching pose challenges to data-driven analyses. By linking Dutch household-level data with policy-initiative-specific data at local level, we present an explorative study on the uptake of a local poverty pass. The goal is to unravel pass usage in terms of household income and location as well as the age of users. We find that income and age play a role in whether the pass is used, and usage differs per neighborhood. With this, the paper feeds into the discourse on how to operationalize and design data matching work in the multidimensional space of poverty and nonmonetary government initiatives.

AbstractFacilitating direct citizen participation through online channels is considered as an opportunity for including harder to reach groups in participation. Because young adults (18‐25) are heavy internet users, this group is expected to be easier to include online. Evidence shows, however, that also in online direct participation young adults remain underrepresented. To better understand this discrepancy from the perspective of participants, this research asks how young adults explain their intention to participate online. Thematic analysis highlights that young adults' intention to participate in online direct participation can be explained through: (1) cause‐oriented participation, (2) the role of social (media) networks, (3) selective attention to cope with information overload, and (4) navigating misinformation and negativity. In the discussion section, this article considers how these explanations can inform government strategies for better including young adults in online direct participation.

AbstractAs online platforms are swiftly becoming a dominant interface of government–citizen interactions, it is crucial that these platforms are inclusive. This article considers the potential of using digital trace data for assessing inclusivity in online platforms. Given that Google Analytics (GA) is a popular tool for collecting and analyzing digital trace data, this article asks to what extent GA is useful in usability evaluation for assessing inclusivity. This article reports on a case study in which GA was used for the usability evaluation of an online participation platform of a local government in The Netherlands. The usability evaluation via GA highlights that the platform was less inclusive toward two categories of users, namely, mobile users and users who access the platform via social media or referral. We conclude that GA is helpful for assessing inclusivity in online government platforms, particularly to the extent that it can be used to monitor whether there are differences in usability for different categories of users. Shortcomings of GA are that it gives limited insights into underlying reasons for identified behavioral patterns, that data quality can be compromised, and that functionalities of GA are restricted based on privacy or legal considerations.

This article uses a theoretical and methodological framework derived from the political theorist Quentin Skinner and the conceptual historian Reinhart Koselleck to examine ideational and conceptual tensions and shifts related to the transparency of algorithmic and other automatic governmental decision-making in Finland. Most of the research material comprises national and international official documents and semi-structured expert interviews. In Finland, the concepts of 'algorithmic transparency' and other 'transparency of automatic decision-making' are situated amongst a complex array of legal, ethical, political, policy-oriented, managerial, and technical semantic fields. From 2016 to 2019 Finland's Deputy Ombudsman of Parliament and the Constitutional Committee of Parliament pinpointed issues in algorithmic and other automatic decision-making with the consequence that at the turn of 2019 and 2020, the Ministry of Justice started moving towards the preparation of new legislation to resolve these issues. In conclusion and as expected, Finland's version of the Nordic tradition of the public sphere with established legal guarantees of public access to government documents indeed has both important enabling and constraining effects upon resolving the transparency issues.

Algorithmic decision-making is neither a recent phenomenon nor one necessarily associated with artificial intelligence (AI), though advances in AI are increasingly resulting in what were heretofore human decisions being taken over by, or becoming dependent on, algorithms and technologies like machine learning. Such developments promise many potential benefits, but are not without certain risks. These risks are not always well understood. It is not just a question of machines making mistakes; it is the embedding of values, biases and prejudices in software which can discriminate against both individuals and groups in society. Such biases are often hard either to detect or prove, particularly where there are problems with transparency and accountability and where such systems are outsourced to the private sector. Consequently, being able to detect and categorise these risks is essential in order to develop a systematic and calibrated response. This paper proposes a simple taxonomy of decision-making algorithms in the public sector and uses this to build a risk management framework with a number of components including an accountability structure and regulatory governance. This framework is designed to assist scholars and practitioners interested in ensuring structured accountability and legal regulation of AI in the public sphere.

The governance of public sector organizations has been challenged by the growing adoption and use of Artificial Intelligence (AI) systems and algorithms. Algorithmic transparency, conceptualized here using the dimensions of accessibility and explainability, fosters the appraisal of algorithms' footprint in decisions of public agencies, and should include impacts on civil servants' work. However, although discretion will not disappear, AI innovations might have a negative impact on how public employees support their decisions. This article is intended to answer the following research questions: RQ1. To what extent algorithms affect discretionary power of civil servants to make decisions?RQ2. How algorithmic transparency can impact discretionary power of civil servants? To do so, we analyze SALER, a case based on a set of algorithms focused on the prevention of irregularities in the Valencian regional administration (GVA), Spain, using a qualitative methodology supported on semi-structured interviews and documentary analysis. Among the results of the study, our empirical work suggests the existence of a series of factors that might be linked to the positive impacts of algorithms on the work and discretionary power of civil servants. Also, we identify different pathways for achieving algorithmic transparency, such as the involvement of civil servants in active development, or auditing processes being recognized by law, among others.

This article examines the relationship between Artificial Intelligence (AI), discretion, and bureaucratic form in public organizations. We ask: How is the use of AI both changing and changed by the bureaucratic form of public organizations, and what effect does this have on the use of discretion? The diffusion of information and communication technologies (ICTs) has changed administrative behavior in public organizations. Recent advances in AI have led to its increasing use, but too little is known about the relationship between this distinct form of ICT and to both the exercise of discretion and bureaucratic form along the continuum from street- to system-levels. We articulate a theoretical framework that integrates work on the unique effects of AI on discretion and its relationship to task and organizational context with the theory of system-level bureaucracy. We use this framework to examine two strongly differing cases of public sector AI use: health insurance auditing, and policing. We find AI's effect on discretion is nonlinear and nonmonotonic as a function of bureaucratic form. At the same time, the use of AI may act as an accelerant in transitioning organizations from street- and screen-level to system-level bureaucracies, even if these organizations previously resisted such changes.

AbstractGovernments are increasingly implementing smart and digital approaches to promoting citizen participation. However, whether online participation platforms are tools that improve inclusivity in citizen participation remains underexplored. To address this gap, this article focuses on the role of recruitment messages and their effect on participation in an online participation platform by gender and age. A field experiment with a neighborhood census sample (N = 6,066) shows that online participation dips for younger and older citizens and is equal among women and men. For the age groups between 60 and 75, differences in the control and intervention recruitment messages significantly impacted participation. These findings can help public managers tailor recruitment strategies to facilitate inclusive participation and represent a first step toward learning what types of messages are effective for whom.

First published as a special issue of Policy & Politics, this updated volume explores policy failures and the valuable opportunities for learning that they offer. Policy successes and failures offer important lessons for public officials, but often they do not learn from these experiences. The studies in this volume investigate this broken link. The book defines policy learning and failure and organises the main studies in these fields along the key dimensions of processes, products and analytical levels. Drawing together a range of experts in the field, the volume sketches a research agenda linking policy scholars with policy practice