Trust is a crucial factor in multi-actor data-sharing initiatives, particularly in sensitive domains like healthcare, where patient privacy, regulatory requirements, and organizational collaboration intersect. However, achieving trust-by-design, creating trust through intentional design choices, is challenging. To address this challenge, this paper investigates how trust frameworks in healthcare data-sharing are designed and how they evolve over time. Central to this inquiry is the conceptualization of “trust anchors”– designable components that provide a foundation for creating trust. Drawing on Technological Innovative Systems theory, this research qualitatively examines two healthcare trust frameworks, each at different lifecycle stages. The case studies reveal how trust anchors contribute to both the development and active management of trust frameworks. The contribution includes a lifecycle approach for trust frameworks and a matrix for categorizing trust anchors, providing guidance for organizations aiming to implement and maintain multi-actor data-sharing frameworks. We find that enforceable trust anchors are more important in the mature phase of a trust frameworks, while in the growing phase, less designable and enforceable trust factors assume a greater role.

Despite their pivotal role in promoting transparency, open data portals often struggle to engage citizens, functioning instead as static ‘data graveyards’. While external activities, such as hackathons, can raise awareness, they do not directly cultivate sustained engagement within the portals. One promising approach to leverage citizens’ engagement motivation is the integration of game elements to transform passive data access into interactive gamified experiences. However, despite its potential, there is limited research on gamified citizens’ motivation to engage with open data portals. This paper examines how static and dynamic game elements are implemented across 31 open data portals. Lastly, we use the Self-Concordance Model to discuss the alignment between motivation, personal values, and game elements. Our findings reveal that most portals incorporate ‘discovery’ elements into their dataset-searching features, subtly gamifying exploration. Additionally, portals emphasising external activities, such as hackathons and events, often lack integrated social features, suggesting a trade-off between external engagement and sustained in-portal interaction. These findings challenge the assumption that open data engagement relies primarily on external initiatives, emphasising in-portal gamification instead. This study provides recommendations for policymakers to engage with users within open data portals.

In response to unprecedented global urbanization, the smart city concept has emerged, leveraging ICT to enhance municipal efficiency and improve the quality of urban life. The concept of smart energy city (SEC) is closely related to smart cities, however, energy system development in a smart city context is often found eluding certain segments of society, which calls for more attention to inclusion in SEC development. In this paper, the research question is: How can inclusion be effectively integrated into a framework of SEC design? A framework is developed comprising three key principles - energy conservation, energy efficiency, and renewable energy. These principles are aligned with collaboration among stakeholders, smart energy solutions applications, and integration of these solutions. The framework is illustrated using two real-world cases of demonstration projects in the City of Amsterdam, the Netherlands. The paper concludes by presenting several strategies for fostering inclusion in SEC development. They pertain to including utilization of the framework as a guideline to promote inclusion, establishing a clear understanding of inclusion, and involving all relevant stakeholders, including citizens' rights from the project's inception, and fostering transparency regarding the objectives, interests, and individual stakeholders' value.

Vulnerable road users (VRUs), including pedestrians, cyclists, and motorcyclists, account for approximately 50% of road traffic fatalities globally, as per the World Health Organization. In these scenarios, the accuracy and fairness of perception applications used in autonomous driving become critical to reduce such risks. For machine learning models, performing object classification and detection tasks, the focus has been on improving accuracy and enhancing model performance metrics; however, issues such as biases inherited in models, statistical imbalances and disparities within the datasets are often overlooked. Our research addresses these issues by exploring class imbalances among vulnerable road users by focusing on class distribution analysis, evaluating model performance, and bias impact assessment. Using popular CNN models and Vision Transformers (ViTs) with the nuScenes dataset, our performance evaluation shows detection disparities for underrepresented classes. Compared to related work, we focus on metric-specific and cost-sensitive learning for model optimization and bias mitigation, which includes data augmentation and resampling. Using the proposed mitigation approaches, we see improvement in IoU(%) and NDS(%) metrics from 71.3 to 75.6 and 80.6 to 83.7 for the CNN model. Similarly, for ViT, we observe improvement in IoU and NDS metrics from 74.9 to 79.2 and 83.8 to 87.1. This research contributes to developing reliable models while addressing inclusiveness for minority classes in datasets. Code can be accessed at: BiasDet.

Vision transformer (ViTs) models have shown higher accuracy, robustness and large volume data processing ability, creating new baselines and references for perception tasks. However, these advantages require large memory and high-performance processors and computing units, which makes model adaptability and deployment challenging within resource-constrained environments such as memory-restricted and battery-powered edge devices. This paper addresses the model deployment challenges by proposing a model approximation approach VI-ViT, for edge deployment using variational inference with mixed precision for processing multi-modalities, such as point clouds and images. Our experimental evaluation on the nuScenes and Waymo datasets show up to 37% and 31% reduction in model parameters and Flops while maintaining a mean average precision of 70.5 compared to 74.8 of the baseline model. This work presents a practical deployment approach for approximating and optimizing Vision Transformers for edge AI applications by balancing model metrics such as parameters, flops, latency, energy consumption, and accuracy, which can easily be adapted to other transformer models and datasets.

Organizations increasingly use Generative Artificial Intelligence (AI) to create strategic documents, legislation, and recommendations to support decision-making. Many current AI initiatives are technology-deterministic, whereas technology co-evolves with the social environment, resulting in new applications and situations. This paper presents a novel view of AI governance by organizations from the perspective of complex adaptive systems (CASs). AI is conceptualized as a socio-technological and adaptive system in which people, policies, systems, data, AI, processes, and other elements co-evolve. The CAS lens draws attention to focusing AI governance on the entire organization, taking an outward perspective and considering public values and societal concerns. Although there is no shortage of AI governance instruments, they differ in their effectiveness, and combinations of appropriate mechanisms should be selected to deal with AI's evolving nature and complexity. A major challenge is that no responsibility, and therefore accountability, is taken due to the lack of understanding of the full socio-technological CAS. As such, joint accountability is needed in which involved parties work together.

Deploying scalable Vision Transformer applications on mobile and edge devices is constrained by limited memory and computational resources. Existing model development and deployment strategies include distributed computing and inference methods such as federated learning, split computing, collaborative inference and edge-cloud offloading mechanisms. While these strategies have deployment advantages, they fail to optimize memory usage and processing efficiency, resulting in increased energy consumption. This paper optimizes energy consumption by introducing adaptive model partitioning mechanisms and dynamic scaling methods for ViTs such as EfficientViT and TinyViT, adjusting model complexity based on the available computational resources and operating conditions. We implement energy-efficient strategies that minimize inter-layer communication for distributed machine learning across edge devices, thereby reducing energy consumption from data flow and computation. Our evaluations on a series of benchmark models show improvements, including up to a 32.6% reduction in latency and 16.6% energy savings, while maintaining mean average precision sacrifices within 2.5 to 4.5% of baseline models. These results show that our proposal is a practical approach for improving edge AI sustainability and efficiency.

The metaverse is still in its early stages, and most prototype metaverse platforms fail to fulfill their potential and meet users’ demands. Numerous studies have focused on the success of metaverse applications. However, there has been limited research on the failure of the metaverse services. Based on digital service failure model (DSFM), uses and gratification theory (U&G), and organizational failure diagnosis model (OFDM), an integrated model to investigate the influences of different failure types on citizens’ satisfaction with meta-government services is developed. The model was tested using a survey of 402 responses in China, which were analyzed using a hybrid structural equation modeling artificial neural network (SEM-ANN) approach. The findings indicated that organizational failure exerts the most powerful effect on citizens’ satisfaction. Moreover, information, service, system, and psychological needs failure, negatively influence citizens’ satisfaction. In addition, technology anxiety can weaken the influences of information, system, psychological needs, and organizational failures on citizens’ satisfaction while enhancing the relationship between service failure and citizens’ satisfaction. This research contributes initial and original insights into the failure types of meta-government services and their impacts, addressing a significant gap in the literature.

Implementing artificial intelligence (AI) in public settings requires a fundamental transformation of various social and technical aspects within public administration. However, the transformative efforts required for AI integration and use in government remain underexplored. This study introduces the concept of 'AI-augmented government transformation,' building on sociomateriality and sociotechnical theory, and develops a theoretical framework to explore this phenomenon. By applying this framework and drawing insights from expert interviews, we identify the strategic shifts and socio-technical adaptations essential for integrating AI into public administrations. Our analysis highlights the importance of opening the 'black box' of AI to gain a deep understanding of its underlying technologies and their materialities. The findings reveal complex interdependencies between AI materiality and the social and technical systems that public administrations must navigate. Specifically, AI, as a novel materiality, introduces new organizational dynamics, enhances employee capabilities, and alters operational routines and practices. These changes complement technical ones, such as upgrades and advancements in data collection and processing. By investigating the complexities of AI-augmented government transformation, this research offers novel and practical insights for policymakers and practitioners navigating the challenges and opportunities of AI integration.

Understanding the landscape of privacy protection in governmental systems is crucial for ensuring the trustworthiness of public services and safeguarding citizens' sensitive data from breaches or misuse. Systematic mapping and synthesis of previous research can help identify existing privacy-preserving techniques, assess their effectiveness, and highlight areas for improvement, offering valuable insights for policymakers and practitioners. We aim to conduct a systematic literature review (SLR) of privacy-preserving tools and technologies, focusing on their adoption and governments' challenges. This study also uncovers emerging trends and future research directions, contributing to developing more robust privacy strategies tailored to government needs.Given its extensive reach and government-centric methodology, this evaluation distinguishes itself from previous research. Our work methodically synthesizes privacy-preserving tools and technologies from the distinct perspective of government roles, in contrast to previous assessments that concentrate narrowly on certain technologies or areas. Our findings offer a synthesis of the government's diverse roles in privacy preservation - regulator, enforcer, user, and service provider - and address existing concerns and key privacy-related technologies. Finally, we identify significant research opportunities, such as improving privacy-preserving mechanisms to strengthen the integrity of public services and mitigate the risks of data breaches and misuse.

This is the 11th edition of the workshop series labeled “Web and the City - The Web and Smart Cities”, which started back in Florence in 2015 and kept on taking place every year in conjunction with the WWW conference series. Last year the workshop was held in Singapore. Each year the focus of the workshop is actualized, and this year the workshop focuses on the Responsible web and Responsible AI in cities and communities. In the era of digital twinning, AI, and the metaverse (so-called citiverse for cities), and under the UN 2030 Agenda for sustainable growth and resilience, cities are being transformed into virtual spaces that enable service automation and value generation to their communities and enterprises. Moreover, AI and web intelligence generate new types of opportunities, including generating automated transactions in these virtual spaces, which must preserve human-perceived consequences, fairness, accountability, transparency, and ethics. This workshop aims to focus on how the Web transforms cities into responsibly intelligent virtual environments.

Despite the many promises of open data, numerous challenges inhibit its full potential, such as its poor or inconsistent quality, a lack of complementary assets, and the limited skills of data providers and end-users. Open data intermediaries are instrumental in addressing some of these challenges. They provide specialized resources and capabilities to enhance the supply, flow, or use of open data, or strengthen the relationships among various open data ecosystem (ODE) actors. However, in-depth studies on open data intermediation business models are limited, with most having only offered birds-eye views of those business models. This deficiency has limited our knowledge to develop open data intermediation business models that contribute positively to the ODE. In the geospatial domain, Esri is an important open data intermediary, having been involved in such a role since the 1990s. This article unpacks Esri's open data intermediation business model and analyses its current strengths and weaknesses as well as its potential opportunities and threats to the ODE. Finally, this article recommends factors to consider in developing an open data intermediation business model that supports the sustainability of the ODE.

Driving assist applications and connected autonomous vehicle systems are supported using AI models and algorithms, which process and analyze heavy data volumes. High-performance computing units and large memory systems support these models, algorithms, and applications, which results in additional onboard energy consumption. The current trend is also towards full electrification of vehicles and increasing connectivity in the vehicular ecosystem to support collaborative and distributed applications using vehicle-edge-cloud computing. However, with the increased focus on model performance and improving the accuracy of these models and applications, the issue of high-performance computing requirements and resulting energy consumption are overlooked. The problem becomes more challenging and complex for resource-constrained edge devices, which are battery-dependent and have limited memory and computing power. This paper proposes components for an adaptive framework to reduce energy consumption by balancing model accuracy. The contributions include proposing and integrating model partition mechanisms, adaptive deployment across edge devices and approximation strategies for the models. By integrating these components, this framework supports energy-aware development across various platforms. The approach offers a sustainable method for computing and communication-oriented applications within the vehicular ecosystem.

A number of government agencies have started deploying the Metaverse to connect better with their constituents. The Metaverse provides a rich interaction environment and has the potential to engage with, especially, the younger generation. However, the Metaverse's potential impact on the government sector has been given limited attention. This discussion paper aims to fill this void by reviewing the state of the art, analyzing possible roles of the Metaverse for governments and providing research directions. We found six facilitators and nine barriers and risks. The Metaverse offers much more than a virtual presence or copy of the physical world; significant transformations are needed in government to reap the benefits. Given the evolution of the Metaverse, government presence also needs to evolve, and different governments make different decisions about their Metaverse presence. We recommend more research into the nature, use, applications, transformations, and implications of the Metaverse on government functioning.

Machine learning (ML) is reshaping customer service, tackling the growing complexity and volume of customer requests. This article investigates the effects of ML on perceived service quality (PSQ) across various customer service measures within an organizational context. Utilizing a quasiexperimental design, we analyzed 131 978 service requests submitted to the service desk of a large enterprise software organization. Over a 2-year period, these requests were made by 1252 organizations and were associated with 85 654 predictions and 19 720 returned PSQ postservice questionnaires. Our regularized logistic regression model aimed to categorize service requests into 56 categories and dispatch them into one of nine assignment groups to enhance resolution efficiency. Contrary to expectations, the overall PSQ did not significantly improve, while five specific metrics, such as time to resolve and first-time resolution, improved. This may be attributed to the increase in the first personal response time. The article highlights the complexities of implementing ML-based classification and underscores the importance of organizational structure. We found that expert groups prioritizing accurate problem-solving over quick responses led to an increase in the response time for incoming service requests. Theoretical contributions include an understanding of how classification in customer service affects PSQ, offers practical tactics to counteract negative impressions, and sets the groundwork for future article on ML in customer service management, despite the limitations, such as the potential influence of external factors and the study's generalizability.

Open Government Data (OGD) has been considered as a potent instrument for value creation and innovation by a range of stakeholders. Given that individual ingenuity is a function of individual and environmental factors, it is important to understand how the OGD adoption and usage is a factor of creative performance behaviors (CPB), viz., Problem Identification (PI), Information Search (IS), Idea Generation (IG) and Idea Promotion (IP) as well as creative self-efficacy (CSE). Invoking the adapted Unified Theory of Acceptance and Use of Technology (UTAUT) constructs alongside the moderating effects of CPB and CSE constructs and also gender, the present study seeks to underline the behavioural intention towards OGD adoption and usage among 362 undergraduate and postgraduate university students in India. The guiding research question is: “Is there any difference among the males and females in terms of their OGD adoption and usage as far as their creative propensities are concerned?” Findings from the PLS-SEM (Partial Least Squares-Structural Equation Modeling) procedures show that there are gender differences across the CPB and CSE constructs. The study's contribution lies in furthering our understanding of OGD adoption and use with the additional determinants of creativity literature.