Introduction to the Digital Government and Artificial Intelligence Minitrack

The role of technology in the public sector continues to evolve. Digital government has emerged as an important research and pedagogical topic in diverse disciplines, including information systems, public administration, computer science and political science. Given the increasing amount of data available to organizations and constituents, exploring the role of artificial intelligence (AI) is imperative for the effective, efficient and ethical use of government resources

Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision-Making

Algorithms based on Artificial Intelligence technologies are slowly transforming street-level bureaucracies, yet a lack of algorithmic transparency may jeopardize citizen trust. Based on procedural fairness theory, this article hypothesizes that two core elements of algorithmic transparency (accessibility and explainability) are crucial to strengthening the perceived trustworthiness of street-level decision-making. This is tested in one experimental scenario with low discretion (a denied visa application) and one scenario with high discretion (a suspicion of welfare fraud). The results show that: (1) explainability has a more pronounced effect on trust than the accessibility of the algorithm; (2) the effect of algorithmic transparency not only pertains to trust in the algorithm itself but also—partially—to trust in the human decision-maker; (3) the effects of algorithmic transparency are not robust across decision context. These findings imply that transparency-as-accessibility is insufficient to foster citizen trust. Algorithmic explainability must be addressed to maintain and foster trustworthiness algorithmic decision-making

Breaking bad news without breaking trust:The effects of a press release and newspaper coverage on perceived trustworthiness

Can a government agency mitigate the negative effect of "bad new" on public trust? To answer this question, we carried out a baseline survey to measure public trust five days before a major press release involving bad news about an error committed by an independent regulatory agency in the Netherlands. Two days after the agency's press release, we carried out a survey experiment to test the effects on public trust of the press release itself as well as related newspaper articles. Results show that the press release had no negative effect on trustworthiness, which may be because the press release "steals thunder" (i.e. breaks the bad news before the news media discovered it) and focuses on a "rebuilding strategy" (i.e. offering apologies and focusing on future improvements). In contrast, the news articles mainly focused on what went wrong, which affected the competence dimension of trust but not the other dimensions (benevolence and integrity). We conclude that strategic communication by an agency can break negative news to people without necessarily breaking trust in that agency. And although effects of negative news coverage on trustworthiness were observed, the magnitude of these effects should not be overstated

Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response

Algorithmic decision-making in government has emerged rapidly in recent years, leading to a surge in attention for this topic by scholars from various fields, including public administration. Recent studies provide crucial yet fragmented insights on how the use of algorithms to support or fully automate decisions is transforming government. This article ties together these insights by applying the theoretical lenses of government legitimacy and institutional design. We identify how algorithmic decision-making challenges three types of legitimacy—input, throughput, and output—and identify institutional arrangements that can mitigate these threats. We argue that there is no silver bullet to maintain legitimacy of algorithmic government and that a multiplicity of different institutional mechanisms is required, ranging from legal structures and civic participation to closer monitoring of algorithmic systems. We conclude with a framework to guide future research to better understand the implications of institutional design for the legitimacy of algorithmic government

Behavioral Public Administration:Combining Insights from Public Administration and Psychology

Behavioral public administration is the analysis of public administration from the micro-perspective of individual behavior and attitudes by drawing upon insights from psychology on behavior of individuals and groups. We discuss how scholars in public administration currently draw on theories and methods from psychology, and related fields, and point to research in public administration that could benefit from further integration. An analysis of public administration topics through a psychological lens can be useful to confirm, nuance or extend classical public administration theories. As such, behavioral public administration complements traditional public administration. Furthermore, it could be a two-way street for psychologists who want to test the external validity of their theories in a political-administrative setting. Finally, we propose four principles to narrow the gap between public administration and psychology

A validated measurement for felt relational accountability in the public sector: gauging the account holder’s legitimacy and expertise

The effectiveness of formal public sector accountability mechanisms is largely predicated on the individual perception of accountability. In particular, the individual’s experienced relationship to account holders is key in understanding the effects of formal accountability mechanisms. This article develops a measurement instrument for felt relational accountability in public administration. We measure perceived legitimacy and expertise of the account holder, as crucial relational dimensions applicable to various accountability relations. The instrument was tested and cross validated among two samples of Dutch public employees. We discuss theoretical implications of studying accountability at the actor-level and provide practical applications of the instrument

Core values for ideal civil servants: Service-oriented, responsive and dedicated

What do citizens want? How do citizens think public servants should behave? Although such questions seem straightforward, little is known about the values citizens expect public servants to uphold. This paper therefore identifies such values through extensive coding of qualitative data from representative samples of United States (n = 395), Dutch (n = 369), and South Korean (n = 379) citizens. Surprisingly, and contrasting to assumptions in the literature on citizen satisfaction, citizens hardly value effectiveness, efficiency, and accountability. In contrast, we found that the top three universal values that citizens desire from public servants are serviceability, responsiveness, and dedication. These values are generic across countries, age, gender, and education level. However, some values are more important in some countries than in others. These differences show the influence of a country's longstanding public administrative tradition and its current situation. Our findings challenge scholars and government officials to rethink what citizens want from their civil servants

‘Just like I thought’: Street-level bureaucrats trust AI recommendations if they confirm their professional judgment

Artificial Intelligence is increasingly used to support and improve street-level decision-making, but empirical evidence on how street-level bureaucrats' work is affected by AI technologies is scarce. We investigate how AI recommendations affect street-level bureaucrats' decision-making and if explainable AI increases trust in such recommendations. We experimentally tested a realistic mock predictive policing system in a sample of Dutch police officers using a 2 × 2 factorial design. We found that police officers trust and follow AI recommendations that are congruent with their intuitive professional judgment. We found no effect of explanations on trust in AI recommendations. We conclude that police officers do not blindly trust AI technologies, but follow AI recommendations that confirm what they already thought. This highlights the potential of street-level discretion in correcting faulty AI recommendations on the one hand, but, on the other hand, poses serious limits to the hope that fair AI systems can correct human biases

The impact of red tape on citizen satisfaction

Red tape is one of the most often-mentioned nuisances of citizens about government. However, there is a dearth in red tape research focusing on citizens. Therefore, the primary goal of this article is to analyze the effect of red tape on citizen satisfaction. The secondary goal is to go beyond testing a linear relationship between red tape and citizen satisfaction by examining individual factors that may moderate this relationship. In order to analyze the red tape-satisfaction relationship, we have designed an experiment in which 179 subjects participated. Experiments are still relatively rare in public administration, but are increasingly seen as a rigorous and useful method for theory testing and development. We found that red tape has a strong negative effect on citizen satisfaction, and that this effect is weaker when citizens have high knowledge of political processes. We conclude with implications and a future research agenda

Research report: Citizen perceptions of regulatory instruments and enforcement styles

This report presents the data collection and preliminary analysis of the twelve focus groups on citizens’ trust in regulation, conducted as part of the TiGRE work package WP5 which focuses on regulatory instruments and enforcement styles of regulatory agencies and how they affect citizen trust in regulatory regimes. The focus groups explore citizens’ views on what constitutes a trustworthy regulator, perceptions regarding the trustworthiness of the food safety regulator, and citizens’ evaluations of particular enforcement styles in six countries: Belgium, Denmark, Germany, Israel, the Netherlands, and Norway. This data collection effort builds on the previous work in WP5, specifically, the survey experiment reported in the deliverable D5.21 , and aims to provide further insights on how citizens’ trust in regulatory agencies is shaped by the behaviours of the regulators. In this report we first discuss the rationale of the focus groups, the data collection approach, and the structure of the focus group discussions. We then present six country reports, which provide summaries of the focus group discussions in each of the six countries. Finally, we provide a preliminary comparative analysis, outlining the similarities and differences emerging from the country reports, and discuss their implications. The results show that citizens consider transparency, integrity, and expertise to be the key traits of a trustworthy regulator. The knowledge of citizens regarding the work of the food safety regulator in their country is rather limited, however, this does not appear to prevent them from placing high levels of trust in its work. When it comes to specific enforcement styles, it does not appear that they have a direct effect on citizens’ trust. What citizens consider to be an appropriate regulatory action in a given situation, would largely depend on the specifics of the situation