Government Information Quarterly最新文献

The objective of this paper is to break down the widely presumed dichotomy, especially in law, between fully automated decisions and human decisions from a psychological and normative perspective. This is particularly relevant as human oversight is seen as an effective means of quality control, including in the current AI Act. The phenomenon of automation bias argues against this assumption. We have investigated this phenomenon of automation bias, as a behavioral effect of and its implications in normative institutional decision-making situations. The phenomenon of automation bias, whereby individuals overly rely on machine-generated decisions or proposals, has far-reaching implications. Excessive reliance may result in a failure to meaningfully engage with the decision at hand, resulting in an inability to detect automation failures, and an overall deterioration in decision quality, potentially up to a net-negative impact of the decision support system. As legal systems emphasize the role of human decisions in ensuring fairness and quality, this paper critically examines the inadequacies of current EU and national legal frameworks in addressing the risks of automation bias. Contributing a novel perspective, this article integrates psychological, technical, and normative elements to analyze automation bias and its legal implications. Anchoring human decisions within legal principles, it navigates the intersections between AI and human-machine interactions from a normative point of view. An exploration of the AI Act sheds light on potential avenues for improvement. In conclusion, our paper proposes four steps aimed at effectively countering the potential perils posed by automation bias. By linking psychological insights, legal analysis, and technical implications, this paper advocates a holistic approach to evolving legal frameworks in an increasingly automated world.

The application of algorithm recommender systems introduces several potential risks, including privacy infringement, discriminatory outcomes, and opacity. Governments worldwide have introduced regulatory policy instruments with varying degrees of coerciveness. However, few studies have examined the impact of regulatory policy instruments on public acceptance. This study investigates the nuanced ways in which regulatory policy instruments spanning a spectrum of coerciveness shape public perceptions of privacy risks and acceptance of algorithm recommender systems. Through a survey experiment involving a sample size of 2015 participants, we created three distinct categories of regulatory policy instruments to serve as experimental treatments and one control group with no intervention. Our empirical findings illustrate the substantial treatment effects stemming from regulatory policy instruments. Furthermore, these regulatory policy instruments, infused with varying degrees of coerciveness, assume a dynamic moderating role, initially mitigating and subsequently intensifying the adverse influence of perceived privacy risks on public acceptance. By synthesizing two streams of literature on technology acceptance and regulatory policy, our research underscores the fact that regulatory policy instruments have the potential to reshape the public's perceived risk associated with technology use, exerting a considerable influence on public technology acceptance. These findings have implications for governments seeking effective, fine-grained algorithmic governance based on individual behavior. Policymakers should consider public risk perceptions and technology acceptance when designing regulatory policies for enterprises.

The rapid advancement of information and communication technologies is driving a swift transition towards digital transformation (DT) in public services globally. This paper investigates Brazil's approach to DT through its Startup Gov.br program, which employs startup principles to drive digital innovation within federal government agencies. Drawing upon interviews with key stakeholders and analysis of publicly available documents, the study reveals the program's characteristics and its integration into the current IT governance structure of the federal government. The results indicate that the program effectively addresses capacity constraints within government agencies, fostering strategic projects and long-term cultural change. By examining this novel approach, the paper contributes to the literature on startups in the public sector and offers insights into how governments can leverage innovative strategies for effective digital transformation.

This study examines the relationship between institutional trustworthiness and public acceptability of Artificial Intelligence (AI)-based Facial Recognition Technology (FRT) on its uses in five U.S. policing scenarios - public protests, large events, public streets, and two artificial scenarios representing public partial and high FRT acceptability. Drawing on a 2021 U.S. nationally representative survey (n = 4679), logit models demonstrate that two institutional trustworthiness dimensions - integrity and ability - affect public FRT acceptability. The effect of integrity is consistent across the five scenarios, and the Average Marginal Effects (AMEs) indicate that the effect is largest when the public assesses FRT acceptability in public protests - that presents the greatest privacy concern to citizens. This study contributes to the emerging literature on AI technology adoption in public entities through the institutional trustworthiness lens. It expands the application of the institutional trustworthiness framework into the local context of U.S. police adopting FRT and highlights contextual implications for public organizations adopting intrusive technologies. This study challenges a pervasive assumption of trading privacy for security in adopting FRT-like intrusive AI technologies in public services and recommends the ethical use of AI-based FRT in its operation, implementation, and development for public institutions, policymakers, and the AI industry.

Previous research on demand-side e-government has not adequately examined the utilization of specific e-government services by different citizen groups, particularly digitally vulnerable ones, via both websites and mobile platforms. This lack of understanding has generated to a limited knowledge base regarding the usage of these services. Furthermore, there is a dearth of longitudinal research in this area, further impeding our understanding of the topic. This study aims to analyze the diffusion and the divide in citizens' use of e-government services over time. Using the repeated cross-sectional survey data (2012−2021) of the Korean citizens' use of the web-based and mobile e-government services, we found that citizens' use of e-government services has generally increased over the past 10 years, with information transaction services (e.g. Gov24) being the most widely used, followed by financial transaction services (e.g. Hometax). However, the speed of e-participation services (e.g. E-people) diffusion and its usage rate over time is much slower and lower than other types of e-government services. Web-based and mobile e-government services have been showing similar trends of change. The usage rate of web-based e-government services, however, has been slightly higher than that of mobile e-government services. The divide in the usage of e-government services for digitally vulnerable groups has been generally decreasing. However, the usage gap of e-participation services is very sensitive to the yearly changes due to its low usage rate, making the pattern of the usage gap change irregularly. Lastly, the divide caused by intersectionality factors such as the elderly with low incomes is far more serious than those caused by single factors. The study's findings emphasize the importance for digital government leaders and managers to focus on citizens' use of e-government services, considering factors such as service type, user group, and platform. This attention is crucial in promoting digital inclusion and equity, ultimately addressing the issue of the divide in the use of e-government services.

The importance of government transparency in reducing corruption has been well documented. However, the effects of different forms of transparency on containing corruption and whether transparency has a blanket effect across all forms of corruption remain less understood. This study examines the impact of agent-controlled transparency on the latency of different types of corruption. Drawing on a dataset of 30,558 corruption-related court judgments in China, this study employs hierarchical linear modeling (HLM) by integrating individual-level data and provincial-level data to yield three major findings. First, agent-controlled transparency is more likely to decrease the concealment of petty corruption than grand corruption. Furthermore, agent-controlled transparency is more likely to reduce the latency of autocorruption than transactive corruption, which is usually disguised in normal social interactions. Third, when the scale and form of corruption are combined into four types, namely petty autocorruption, grand autocorruption, petty transactive corruption, and grand transactive corruption, agent-controlled transparency is more likely to reduce the latent period of petty autocorruption than that of other types.

Public participation is crucial for the governance of artificial intelligence (AI). However, the public is typically portrayed as a passive recipient in practice, and little is known about their expectations, assumptions and knowledge about AI. Based on the theoretical lens of technological frames, and using Latent Dirichlet Allocation (LDA) and content coding on 114,393 relevant comments, the article explores public perceptions of the nature of AI, AI strategy and AI in use, and further compares the discourse before and after the launch of ChatGPT. The findings show that ChatGPT amplifies public enthusiasm and expectations of AI, as well as concerns and fears, and draws attention to national competition. However, public discourses are generally conflicting and preliminary, characterized by abstract and grandiose narratives, posing challenges in reaching consensus and generating practical insights. Therefore, this analysis raises serious concerns over public participation in AI governance, as the public not only faces limited channels for substantive involvement but also struggles to articulate effective discourses in the public sphere.

This study draws on the issue expansion model and symbolism, both of which are influential concepts in the literature of public policy and agenda setting, to generate textual features for developing a predictive model of online petition success. Using a real-life dataset of an online petition platform, we show that the proposed model performs well in several important evaluation metrics when compared with benchmark models. This study offers several contributions. First, we present how to translate these concepts into textual features of petitions that can be understood by computers to improve prediction of petition success. The predictive models developed and the patterns of online petitioning identified enhance our understanding of collective actions on online petition platforms. In addition, we demonstrate that we can develop a better predictive model by adopting both supervised and unsupervised approaches of model development together with datasets that are exogenous from online petition platforms. Further examination of the predictive models in future may enable us to define vague concepts in a systematic way. On practical implications, our proposed text-mining model enables policy makers to handle a large volume of social data in a relatively objective manner. This is conducive to civic participation in e-democracy. The model may help policy makers identify potentially popular issues and prevent issue expansion at an early stage to mitigate the possible incursion of social cost. Moreover, by developing a predictive model based on our approach, citizens can compare different petition texts to determine their chances of success and post texts that have a higher predicted rate of success.

Over the years, governments have leveraged various technologies to streamline internal operations for greater efficiency and to create value through digitally delivered public services (i.e., digital services) for several stakeholders, including citizens. While prior studies have contributed to understanding the institutional and organizational determinants of government digital services, much less is known about their utilization and the actual public value they generate from the perspective of the citizen, particularly in developing countries. Some research on technology, economic, and other social divides even suggests that both the use and value of digital services are potentially limited. In this paper, we apply the Delone and McLean model, a well-established information systems theory, to examine citizens' use of government digital services and their perceptions of public value derived from such use. Based on data from a comprehensive national phone survey across 32 Mexican States with 4300 respondents, our structural equation modeling reveals that system quality is the most significant predictor of use and user satisfaction with digital services. Furthermore, use and user satisfaction lead to public value as measured by the conveniences, flexibilities, and process simplifications the system affords. Our study contributes to digital government literature by using the concept of public value as a measure of digital government services' impact and success from the perspective of the citizens.

The number of studies evaluating e-participation levels in e-government services has recently increased. These studies primarily examine stakeholders' acceptance and adoption of e-government initiatives. However, it is equally important to understand whether and how value is generated through e-participation, regardless of whether the focus is on government efforts or user adoption/acceptance levels. There is a need in the literature for a synthesis focusing on e- participation's connection with public value creation using a systematic and comprehensive approach. This study employs a systematic literature review to collect, examine, and synthesize prior findings, aiming to investigate public value creation through e-participation initiatives, including their facilitators and barriers. By reviewing sixty-four peer-reviewed studies indexed by Web of Science and Scopus, this research demonstrates that e-participation initiatives and efforts can generate public value. Nevertheless, several factors are pivotal for the success and sustainability of these initiatives. The study's findings could guide researchers and practitioners in comprehending the determinants and barriers influencing the success and sustainability of e-participation initiatives in the public value creation process while highlighting potential future research opportunities in this domain.