Road safety is a major global concern, as millions of lives are lost every year because of road accidents. Towards an effort to increase road safety, several Internet-of-Vehicle systems have been developed over the last years in order to better monitor vehicle and driver behavior and issue warnings that effectively prevent life-threatening accidents. These systems face a number of challenges including connectivity issues and high installation and/or maintenance costs. The current work introduces the ODOS2020 system, an integrated Internet-of-Vehicles system aiming to increase road safety. The system comprises several On-the-Road Units for vehicle-related data collection from affordable, energy-efficient magnetometers and calculation of critical parameters, such as each passing vehicle’s speed and direction. A Road-Side Unit accumulates data from the On-the-Road Units, sends data to a cloud infrastructure for further analysis and sends dedicated warnings to the drivers based on their road behavior and/or specific traffic conditions via a dedicated Human–Machine Interface. The overall system architecture and the key features of its modules are being presented, as well as the evaluation results of specially designed tests performed in an actual motorway under real use case scenarios. The evaluation results showed both a very good technical performance of the system and a high level of user acceptance. This in turn means that the system can be employed for effective traffic control and road accident avoidance via monitoring of critical vehicle parameters and early warning of the drivers based on their and other drivers’ behavior, road conditions and real-time, unpredictable events.

User is one of the most important stakeholder cluster and its participation can link the end of life and early stages in the life cycle of each product when considering the adoption of a circular business model. This chapter presents the main elements of the customer engagement, as identified through a State-of-the-Art analysis carried out in the context of FENIX, as well as those electronic tools in which they will be integrated together with conventional tools for the conduction of commercial activities and the tools to facilitate the interaction with the other actors and activities of FENIX within a single access point digital platform (FENIX Marketplace). The SoA analysis identified the motivational factors that promote a greater customer engagement for the participation throughout all business routes (B2B, B2C but also C2C) applicable in the project. These strategies are improved and enhanced using benefits provided by the social media for the participation in the process. The customer involvement is directly linked to the motives provided within FENIX Marketplace.

Artificial intelligence, machine learning, and deep learning as a service have become the status quo for many industries, leading to the widespread deployment of models that handle sensitive data. Well-performing models, the industry seeks, usually rely on a large volume of training data. However, the use of such data raises serious privacy concerns due to the potential risks of leaks of highly sensitive information. One prominent threat is the Membership Inference Attack, where adversaries attempt to deduce whether a specific data point was used in a model's training process. An adversary's ability to determine an individual's presence represents a significant privacy threat, especially when related to a group of users sharing sensitive information. Hence, well-designed privacy-preserving machine learning solutions are critically needed in the industry. In this work, we compare the effectiveness of L2 regularization and differential privacy in mitigating Membership Inference Attack risks. Even though regularization techniques like L2 regularization are commonly employed to reduce overfitting, a condition that enhances the effectiveness of Membership Inference Attacks, their impact on mitigating these attacks has not been systematically explored.

This chapter examines the evolving relationships between data, AI, and democracy, exploring how emerging technologies are transforming governance and civic participation. While AI holds substantial promise—enhancing public service delivery, facilitating citizen engagement, and streamlining communication between institutions and constituents—it also introduces significant risks, including algorithmic bias, misinformation, surveillance, and corporate dominance. These dual dynamics frame the chapter’s analysis, which is anchored in both theoretical foundations and empirical observations. In light of this, the chapter begins by unpacking the promise and risk associated with AI in democratic contexts, situating current debates within the broader landscape of civic technologies and e-participation. It then examines the state of the art through two key sources: the 2025 Digital Participation Tools Ratings by People Powered and the IPU's 2025 report on AI use in parliaments. Finally, the chapter situates four EU-funded initiatives—ORBIS, KT4D, AI4GOV, and ITHACA—within this landscape, highlighting their contributions to advancing democratic innovation through technological, ethical, and participatory approaches.

This chapter examines the ethical dimensions of data use in democratic contexts, focusing on the responsibilities and dilemmas that arise as data and AI systems become embedded in public decision-making. While previous chapters explored the potential of data to enhance participation and transparency, this chapter turns to the normative foundations that should guide such processes. It discusses key ethical principles—such as transparency, accountability, fairness, privacy and explainability—and considers how these values can be upheld in both policy and technical design. The chapter emphasizes that data ethics requires ongoing reflection, inclusive governance and public engagement. To ground these discussions, the chapter illustrates how different projects deal with data management and ethics in practice. These cases offer insights into the tensions, trade-offs and innovative approaches that emerge when aligning data-driven innovation with democratic values and underscore the importance of ethics as a dynamic and context-aware process.