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The rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements. However, they all rely on data which is not only big, open and linked but varied, dynamic and streamed at high speeds in real-time. Managing such data is challenging. To overcome such challenges and utilize opportunities for BDAS, organizations are increasingly developing advanced data governance capabilities. This paper reviews challenges and approaches to data governance for such systems, and proposes a framework for data governance for trustworthy BDAS. The framework promotes the stewardship of data, processes and algorithms, the controlled opening of data and algorithms to enable external scrutiny, trusted information sharing within and between organizations, risk-based governance, system-level controls, and data control through shared ownership and self-sovereign identities. The framework is based on 13 design principles and is proposed incrementally, for a single organization and multiple networked organizations.

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The development of smart mobility initiatives requires specialized and contextualized policies addressing the needs and interests of many stakeholders involved. Since the development of such policies is challenging, there is a need to learn from the experience of many cities around the world offering efficient and successfully adopted smart mobility services. However, in practice, the information provided about such initiatives is shallow and unstructured. To address this issue, we study the state of the art in mobility services, reviewing scientific publications and 42 smart mobility services delivered by nine smart cities around the world, and we propose a taxonomy for planning and designing smart mobility services. The taxonomy provides a common vocabulary to discuss and share information about such services. It comprises eight dimensions: type of services, maturity level, users, applied technologies, delivery channels, benefits, beneficiaries, and common functionality. The contribution of the proposed taxonomy is to serve as a tool for guiding policy makers by identifying a spectrum of mobility services that can be provided, to whom, what technologies can be used to deliver them, and what is the delivered public value so to justify their implementation. In addition, the taxonomy can also assist researchers in further developing the domain. By identifying common functionality, it could also help Information Technology (IT) teams in building and maintaining smart mobility services. Finally, we further discuss usage scenarios of the taxonomy by policy makers, IT staff and researchers.

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