Government Information Quarterly最新文献

A more secure framework for open government data sharing based on federated learning 基于联合学习的更安全的开放式政府数据共享框架

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-11-11 DOI: 10.1016/j.giq.2024.101981

Xingsen Zhang

Open government data, abbreviated as OGD, attracts significant public interest with substantial social value recently, which enables the government to make more accurate and efficient decisions based on real and comprehensive data. It also helps break down information silos, improve service quality and management efficiency, and enhance public trust in government activities. This is crucial for advancing public management modernization, fostering technological innovation, and strengthening governance capabilities. The focus of this study is how to solve the problem of more secure sharing of OGD. And we developed a more secure framework for open government data sharing based on federated learning. Inspired by the government data authorization operation model, this framework includes four categories of participants: OGD providers, OGD collectors, OGD operators, and OGD users. We further analyzed modeling techniques for horizontal federated learning, vertical federated learning, and federated transfer learning. By applying this framework to typical scenarios in China, its actual effectiveness has been illustrated in preventing information leakage, protecting data privacy, and improving model security, providing more reliable and efficient solutions for government governance and public services. Future research can continuously explore the application of privacy-computing-related technologies in secure sharing of OG to further enhance data security and the potential of OGD.

开放式政府数据（简称 OGD）近来备受公众关注，具有巨大的社会价值，使政府能够基于真实、全面的数据做出更加准确、高效的决策。它还有助于打破信息孤岛，提高服务质量和管理效率，增强公众对政府活动的信任。这对于推进公共管理现代化、促进技术创新和加强治理能力至关重要。本研究的重点是如何解决更安全地共享 OGD 的问题。我们开发了一个基于联合学习的更安全的政府数据开放共享框架。受政府数据授权运行模式的启发，该框架包括四类参与者：开放政府数据提供者、开放政府数据收集者、开放政府数据操作者和开放政府数据使用者。我们进一步分析了横向联合学习、纵向联合学习和联合转移学习的建模技术。通过将该框架应用于中国的典型场景，说明其在防止信息泄露、保护数据隐私、提高模型安全性等方面的实际效果，为政府治理和公共服务提供更可靠、更高效的解决方案。未来的研究可以继续探索隐私计算相关技术在OG安全共享中的应用，以进一步提高数据的安全性和OGD的潜力。

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引用次数: 0

Does trust in government moderate the perception towards deepfakes? Comparative perspectives from Asia on the risks of AI and misinformation for democracy 对政府的信任会缓和对深度假新闻的看法吗？从亚洲的比较视角看人工智能和错误信息对民主的风险

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-10-19 DOI: 10.1016/j.giq.2024.101980

Taberez Ahmed Neyazi , Arif Hussain Nadaf , Khai Ee Tan , Ralph Schroeder

There have recently been growing global concerns about misinformation, and more specifically about how deepfake technologies have been used to run disinformation campaigns. These concerns, in turn, have influenced people's perceptions of deepfakes, often associating them with threats to democracy and fostering less positive views. But does high trust in government mitigate these influences, thereby strengthening positive perceptions of deepfakes? In a cross-national survey conducted in Malaysia, Singapore, and India, we found no evidence of a negative association either between concern about the spread of misinformation online or perceived risks of AI to democracy, with positive attitudes towards deepfakes. However, when accounting for the moderating factor of trust in government, respondents in Singapore who have high trust levels exhibited more positive attitudes towards deepfakes, despite their concerns about misinformation. Similarly, higher trust in government correlated with more favorable perceptions of deepfakes even among those who view AI as a risk to democracy; this effect is evident across all three countries. In the conclusion, we spell out the implications of these findings for politics in Asia and beyond.

最近，全球对虚假信息的关注与日俱增，更具体地说，是对深度伪造技术如何被用于虚假信息宣传的关注与日俱增。这些担忧反过来又影响了人们对深度伪造技术的看法，往往将其与对民主的威胁联系在一起，助长了不太积极的观点。但是，对政府的高度信任是否会减轻这些影响，从而加强人们对深度假新闻的正面看法呢？在马来西亚、新加坡和印度进行的一项跨国调查中，我们没有发现任何证据表明，对网上错误信息传播的担忧或人工智能对民主的风险感知与对深度假新闻的积极态度之间存在负相关。然而，当考虑到对政府信任这一调节因素时，尽管新加坡的受访者对错误信息表示担忧，但信任度高的受访者对深度假新闻表现出更积极的态度。同样，即使在那些认为人工智能对民主构成风险的受访者中，对政府的较高信任度也与对深度假新闻的较好看法相关；这种效应在所有三个国家都很明显。在结论部分，我们阐述了这些发现对亚洲及其他地区政治的影响。

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引用次数: 0

Open government data and self-efficacy: The empirical evidence of micro foundation via survey experiments 开放政府数据与自我效能：通过调查实验的微观基础实证

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-10-18 DOI: 10.1016/j.giq.2024.101975

Kuang-Ting Tai , Pallavi Awasthi , Ivan P. Lee

Research on the potential impacts of government openness and open government data is not new. However, empirical evidence regarding the micro-level impact, which can validate macro-level theories, has been particularly limited. Grounded in social cognitive theory, this study contributes to the literature by empirically examining how the dissemination of government information in an open data format can influence individuals' perceptions of self-efficacy, a key predictor of public participation. Based on two rounds of online survey experiments conducted in the U.S., the findings reveal that exposure to open government data is associated with decreased perceived self-efficacy, resulting in lower confidence in participating in public affairs. This result, while contrary to optimistic assumptions, aligns with some other empirical studies and highlights the need to reconsider the format for disseminating government information. The policy implications suggest further calibration of open data applications to target professional and skilled individuals. This study underscores the importance of experiment replication and theory development as key components of future research agendas.

关于政府开放和开放政府数据的潜在影响的研究并不新鲜。然而，能够验证宏观理论的微观层面影响的实证证据却特别有限。本研究以社会认知理论为基础，通过实证研究以开放数据格式传播政府信息如何影响个人的自我效能感（公众参与的关键预测因素），为相关文献做出了贡献。基于在美国进行的两轮在线调查实验，研究结果显示，接触开放式政府数据与感知自我效能下降有关，从而导致参与公共事务的信心下降。这一结果虽然与乐观的假设相反，但与其他一些实证研究相一致，并强调了重新考虑政府信息传播形式的必要性。政策影响建议进一步校准开放数据应用，以专业技能人才为目标。本研究强调了实验复制和理论发展作为未来研究议程关键组成部分的重要性。

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引用次数: 0

Transforming towards inclusion-by-design: Information system design principles shaping data-driven financial inclusiveness 通过设计实现包容性转型：信息系统设计原则塑造数据驱动的金融包容性

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-10-17 DOI: 10.1016/j.giq.2024.101979

Reni Sulastri, Marijn Janssen, Ibo van de Poel, Aaron Ding

Digitalization and datafication of financial systems result in more efficiency, but might also result in the exclusions of certain groups. Governments are looking for ways to increase inclusions and leave no one behind. For this, they must govern an organizational ecosystem of public and private parties. We derive value-based requirements through a systematic research methodology and iteratively refine design principles for achieving inclusivity goals. This refinement process is enriched by interviews with field experts, leading to the formulation of key Design principles: the essential role of inclusive metrics, leveraging alternative data sources, ensuring transparency in loan processes and the ability for decision contestation, providing tailored credit solutions, and maintaining long-term system sustainability. The government's role is to ensure a level playing field where all parties have equal access to the data. Following the principles ensures that exclusion and discrimination become visible and can be avoided. This study underscores the necessity for system-level transformations, inclusion-by-design, and advocacy for a new system design complemented by regulatory updates, new data integration, inclusive AI, and organizational collaborative shifts. These principles can also be used in different data-driven governance situations.

金融系统的数字化和数据化提高了效率，但也可能导致某些群体被排除在外。各国政府正在想方设法提高包容性，不让任何人掉队。为此，他们必须管理一个由公共和私营各方组成的组织生态系统。我们通过系统的研究方法得出基于价值的要求，并反复推敲实现包容性目标的设计原则。通过与实地专家的访谈，我们丰富了这一完善过程，最终制定了关键的设计原则：包容性衡量标准的重要作用、利用替代数据源、确保贷款流程的透明度和决策争议能力、提供量身定制的信贷解决方案，以及保持系统的长期可持续性。政府的职责是确保公平的竞争环境，使各方都能平等地获取数据。遵循这些原则可确保排斥和歧视现象变得明显并得以避免。这项研究强调，必须进行系统层面的变革，通过设计实现包容性，并倡导新的系统设计，辅之以监管更新、新的数据整合、包容性人工智能和组织合作转变。这些原则也可用于不同的数据驱动治理情况。

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引用次数: 0

Bridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector 缩小差距：为公共部门的人工智能驱动决策开发扩展工具包

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-10-11 DOI: 10.1016/j.giq.2024.101976

Unai Fischer-Abaigar , Christoph Kern , Noam Barda , Frauke Kreuter

AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, these systems face the challenge of aligning machine learning (ML) models with the complex realities of public sector decision-making. In this paper, we examine five key challenges where misalignment can occur, including distribution shifts, label bias, the influence of past decision-making on the data side, as well as competing objectives and human-in-the-loop on the model output side. Our findings suggest that standard ML methods often rely on assumptions that do not fully account for these complexities, potentially leading to unreliable and harmful predictions. To address this, we propose a shift in modeling efforts from focusing solely on predictive accuracy to improving decision-making outcomes. We offer guidance for selecting appropriate modeling frameworks, including counterfactual prediction and policy learning, by considering how the model estimand connects to the decision-maker's utility. Additionally, we outline technical methods that address specific challenges within each modeling approach. Finally, we argue for the importance of external input from domain experts and stakeholders to ensure that model assumptions and design choices align with real-world policy objectives, taking a step towards harmonizing AI and public sector objectives.

人工智能驱动的决策系统正在成为公共部门的重要工具，其应用领域涵盖刑事司法、社会福利、金融欺诈检测和公共卫生等。虽然这些系统为机构决策过程提供了巨大的潜在好处，如提高效率和可靠性，但这些系统也面临着将机器学习（ML）模型与公共部门决策的复杂现实相协调的挑战。在本文中，我们研究了可能出现不匹配的五大挑战，包括分布偏移、标签偏差、数据方面过去决策的影响，以及模型输出方面的竞争目标和人为环路。我们的研究结果表明，标准的 ML 方法往往依赖于无法充分考虑这些复杂性的假设，从而可能导致不可靠和有害的预测。为了解决这个问题，我们建议将建模工作从单纯关注预测准确性转向改善决策结果。通过考虑模型估计值与决策者效用之间的联系，我们为选择适当的建模框架（包括反事实预测和政策学习）提供了指导。此外，我们还概述了应对每种建模方法中特定挑战的技术方法。最后，我们论证了来自领域专家和利益相关者的外部意见的重要性，以确保模型假设和设计选择符合现实世界的政策目标，从而朝着协调人工智能和公共部门目标的方向迈出一步。

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引用次数: 0

Coping with digital transformation in frontline public services: A study of user adaptation in policing 应对一线公共服务的数字化转型：警务用户适应性研究

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-10-10 DOI: 10.1016/j.giq.2024.101977

Muhammad Afzal , Panos Panagiotopoulos

Research on digital transformation has focused on organizational aspects with less attention to the impacts on frontline public officials' daily work. Drawing on the coping model of user adaptation, we investigate how public officials cope with digitalization initiatives and the role of discretion in the coping process. The empirical study focuses on policing and the responses of law enforcement officials (n = 292) in the Punjab region of Pakistan following the implementation of an integrated Police Station Record Management System (PSRMS). Police officers adopted diverse problem-focused and emotion-focused coping strategies. We examine the relationship between these user adaptation strategies and officers' perceptions of system outcomes in improving performance. The study extends coping theory in the context of frontline digital government interactions and offers guidance on how to better embed systems like the PSRMS in public officials' daily work.

有关数字化转型的研究主要集中在组织方面，而较少关注对一线公职人员日常工作的影响。借鉴用户适应的应对模式，我们研究了公职人员如何应对数字化举措以及自由裁量权在应对过程中的作用。实证研究的重点是巴基斯坦旁遮普地区的警务工作以及执法人员（n = 292）在实施综合警察局记录管理系统（PSRMS）后的反应。警务人员采取了以问题为中心和以情绪为中心的不同应对策略。我们研究了这些用户适应策略与警官对系统在提高绩效方面的成果的看法之间的关系。这项研究在一线数字政府互动的背景下扩展了应对理论，并为如何更好地将 PSRMS 等系统嵌入公职人员的日常工作提供了指导。

引用次数: 0

Ironies of automation and their implications for public service automation 自动化的讽刺及其对公共服务自动化的影响

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-09-25 DOI: 10.1016/j.giq.2024.101974

Ida Lindgren

Automation of public service provision has gained renewed attention as emerging technologies are said to enable automation of tasks that were previously seen as requiring human involvement. However, the merits of these automation technologies are often exaggerated. More knowledge is needed on public service automation, and much can be learned from adjacent research fields studying human-automation interaction. To lead by example, this work applies Bainbridge's (1983) concept of ironies of automation. The purpose is to (1) present ironies of automation, (2) explicate how these ironies can come into play when implementing automated systems in the public service context, and (3) outline implications that follow for public service automation. This is achieved by relating ironies of automation to contemporary studies on Robotic Process Automation (RPA) developments in Swedish local government. The analysis results in five ironies and a set of implications for public service automation. The ironies and implications for public service automation direct attention to key challenges that must be acknowledged in future automation implementations and show that further investigations and theoretical developments are needed on e.g., problems introduced by automation; tasks, roles, and responsibilities that follow on automation; how to design the interface between humans and automated systems in a way that facilitates monitoring, take-over, and maintenance; and, tools and methods for assessing the impact and quality of automated systems. This paper thus provides a foundation for future empirical investigations and further theoretical development on public service automation.

据说，新兴技术能够实现以往被视为需要人力参与的任务的自动化，因此，公共服务提供的自动化再次受到关注。然而，这些自动化技术的优点往往被夸大了。我们需要更多关于公共服务自动化的知识，而且可以从研究人机交互的邻近研究领域学到很多东西。为了以点带面，本研究采用了 Bainbridge（1983 年）提出的 "自动化的讽刺 "概念。其目的是：（1）提出自动化的讽刺；（2）解释在公共服务环境中实施自动化系统时，这些讽刺如何发挥作用；（3）概述其对公共服务自动化的影响。为此，我们将自动化的讽刺与瑞典地方政府机器人流程自动化（RPA）发展的当代研究联系起来。通过分析，我们得出了五种具有讽刺意味的现象以及对公共服务自动化的一系列影响。这些对公共服务自动化的讽刺和启示引导人们关注未来自动化实施过程中必须承认的关键挑战，并表明需要进一步调查和理论发展，例如自动化带来的问题；自动化带来的任务、角色和责任；如何设计人类与自动化系统之间的界面，以促进监控、接管和维护；以及评估自动化系统影响和质量的工具和方法。因此，本文为未来的实证调查和公共服务自动化的进一步理论发展奠定了基础。

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引用次数: 0

Explainable AI for government: Does the type of explanation matter to the accuracy, fairness, and trustworthiness of an algorithmic decision as perceived by those who are affected? 为政府提供可解释的人工智能：在受影响者看来，解释的类型对算法决策的准确性、公平性和可信度有影响吗？

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-09-13 DOI: 10.1016/j.giq.2024.101965

Naomi Aoki , Tomohiko Tatsumi , Go Naruse , Kentaro Maeda

Amidst concerns over biased and misguided government decisions arrived at through algorithmic treatment, it is important for members of society to be able to perceive that public authorities are making fair, accurate, and trustworthy decisions. Inspired in part by equity and procedural justice theories and by theories of attitudes towards technologies, we posited that the perception of these attributes of decisions is influenced by the type of explanation offered, which can be input-based, group-based, case-based, or counterfactual. We tested our hypotheses with two studies, each of which involved a pre-registered online survey experiment conducted in December 2022. In both studies, the subjects (N = 1200) were officers in high positions at stock companies registered in Japan, who were presented with a scenario consisting of an algorithmic decision made by a public authority: a ministry's decision to reject a grant application from their company (Study 1) and a tax authority's decision to select their company for an on-site tax inspection (Study 2). The studies revealed that offering the subjects some type of explanation had a positive effect on their attitude towards a decision, to various extents, although the detailed results of the two studies are not robust. These findings call for a nuanced inquiry, both in research and practice, into how to best design explanations of algorithmic decisions from societal and human-centric perspectives in different decision-making contexts.

在人们担心政府通过算法处理做出的决策存在偏见和误导的同时，让社会成员感受到公共机构做出的决策是公平、准确和值得信赖的，这一点非常重要。受公平和程序正义理论以及对技术的态度理论的部分启发，我们假设，对决策的这些属性的感知会受到所提供的解释类型的影响，解释类型可以是基于输入的、基于群体的、基于案例的或基于反事实的。我们通过两项研究验证了我们的假设，每项研究都涉及一项于 2022 年 12 月进行的预先登记的在线调查实验。在这两项研究中，受试者（N = 1200）都是在日本注册的股份公司的高级职员，他们被展示给一个由公共机构做出的算法决策组成的场景：一个部委决定拒绝其公司的拨款申请（研究 1），以及一个税务机关决定选择其公司进行现场税务检查（研究 2）。这两项研究显示，向受试者提供某种解释在不同程度上对他们对决定的态度产生了积极影响，尽管这两项研究的详细结果并不可靠。这些发现要求我们在研究和实践中进行细致入微的探索，研究如何在不同的决策背景下，从社会和以人为本的角度出发，为算法决策提供最佳解释。

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引用次数: 0

Integral system safety for machine learning in the public sector: An empirical account 公共部门机器学习的整体系统安全：经验之谈

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-08-23 DOI: 10.1016/j.giq.2024.101963

J. Delfos (Jeroen), A.M.G. Zuiderwijk (Anneke), S. van Cranenburgh (Sander), C.G. Chorus (Caspar), R.I.J. Dobbe (Roel)

This paper introduces systems theory and system safety concepts to ongoing academic debates about the safety of Machine Learning (ML) systems in the public sector. In particular, we analyze the risk factors of ML systems and their respective institutional context, which impact the ability to control such systems. We use interview data to abductively show what risk factors of such systems are present in public professionals' perceptions and what factors are expected based on systems theory but are missing. Based on the hypothesis that ML systems are best addressed with a systems theory lens, we argue that the missing factors deserve greater attention in ongoing efforts to address ML systems safety. These factors include the explication of safety goals and constraints, the inclusion of systemic factors in system design, the development of safety control structures, and the tendency of ML systems to migrate towards higher risk. Our observations support the hypothesis that ML systems can be best regarded through a systems theory lens. Therefore, we conclude that system safety concepts can be useful aids for policymakers who aim to improve ML system safety.

本文将系统理论和系统安全概念引入到当前有关公共部门机器学习（ML）系统安全的学术讨论中。特别是，我们分析了影响此类系统控制能力的 ML 系统风险因素及其各自的制度背景。我们利用访谈数据，归纳出在公共专业人员的认知中存在哪些此类系统的风险因素，以及哪些因素是基于系统理论所预期但却缺失的。基于从系统理论的视角来看待人工乐虎国际手机版下载系统最合适的假设，我们认为，在解决人工乐虎国际手机版下载系统安全问题的持续努力中，缺失的因素值得更多关注。这些因素包括安全目标和约束条件的阐述、将系统因素纳入系统设计、安全控制结构的开发以及 ML 系统向高风险迁移的趋势。我们的观察结果支持这样的假设，即从系统理论的角度来看待多式联运系统是最合适的。因此，我们得出结论，系统安全概念可以为旨在提高多式联运系统安全的决策者提供有用的帮助。

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引用次数: 0

Untangling the web between digital citizen empowerment, accountability and quality of participation experience for e-government: Lessons from India 解开数字公民赋权、问责制和电子政务参与体验质量之间的联系：印度的经验教训

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-08-22 DOI: 10.1016/j.giq.2024.101964

Swapnil Sharma , Arpan K. Kar , M.P. Gupta

In the digital era, governance is undergoing a transformation, moving state–citizen engagement into online realms, where citizens serve as users and collaborators in shaping services and policies. Empowering citizens to act as social innovators on issues affecting their lives and local communities is the key to facilitate this transition. As interactions between the state and citizens become more convenient, governments are increasingly focusing on digital citizen empowerment (DCE) to improve the life of their populace. Our study aims to understand the different dimensions of DCE and how it leads to better participation. It also aims to study the role of people's perception towards accountability mechanisms in place and how they can pave the way to enhanced participation behaviour. Employing a mixed-method approach, the study utilises structural equation modelling to examine the relation among e-participation, DCE, and public and social accountability. The results conceptualise DCE, identifying its four dimensions: emotional, cognitive, relational, and behavioural. Furthermore, it underscores the significance of citizens' perceptions of governmental and social accountability in fostering e-participation. These findings are subsequently validated through a focus group discussion involving specialists from relevant fields. The results indicate that behavioural empowerment stands out as the most crucial aspect of DCE and that DCE enhances the quality of participation, with accountability mechanisms playing a pivotal role in achieving this outcome. Additionally, the findings reveal public disenchantment with e-government initiatives due to perceived administrative unresponsiveness. By pinpointing specific dimensions of individual empowerment, this study provides insights for policymakers to deliver accountable e-government services that promote enhanced e-participation.

在数字时代，治理正在经历一场变革，国家与公民的参与正在向在线领域转移，公民作为用户和合作者参与服务和政策的制定。赋予公民权力，让他们在影响其生活和当地社区的问题上发挥社会创新者的作用，是促进这一转变的关键。随着国家与公民之间的互动变得越来越方便，政府越来越重视数字公民赋权（DCE），以改善民众的生活。我们的研究旨在了解数字公民赋权的不同层面，以及它如何带来更好的参与。本研究还旨在研究人们对现有问责机制的看法所起的作用，以及这些机制如何为加强参与行为铺平道路。本研究采用混合方法，利用结构方程模型来研究电子参与、民主参与和公共及社会问责之间的关系。研究结果提出了 "情感参与 "的概念，确定了其四个维度：情感、认知、关系和行为。此外，它还强调了公民对政府和社会责任的认知在促进电子参与方面的重要性。随后，相关领域专家参与的焦点小组讨论验证了这些研究结果。结果表明，行为赋权是电子参与的最重要方面，电子参与提高了参与的质量，而问责机制在实现这一结果方面发挥了关键作用。此外，研究结果还显示，由于认为行政部门反应迟钝，公众对电子政务举措不感兴趣。本研究通过指出个人赋权的具体层面，为决策者提供了提供负责任的电子政务服务以促进电子参与的见解。

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引用次数: 0