Abstract: This chapter is concerned with how social order is established within collectives and the ethical problems that arise when we attempt to create and direct collectives towards particular ends. It draws on our work to establish governance principles for Smart Society—an EU project aiming to engineer Collective Adaptive Systems comprised of people and machines with diverse capabilities and goals that are able to tackle societal grand challenges. We examine how social values are implicated in and transformed by Collective Adaptive Systems, and suggest approaches to multilevel governance design that are responsive to emergent capabilities and sensitive to conflicting perspectives. Finally we illustrate our approach with a worked example of a sensor-based system in a care setting.

Citation: Hartswood, M., Grimpe, B., and Jirotka, M., “Towards the ethical governance of Smart Society”, In Miorandi, D., Maltese, V., Rovatsos, M., Nijholt., A. and Stewart, J. (eds) Social collective intelligence: Combining the powers of humans and machines Springer, 2014.

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Abstract: The impact of Social Collective Intelligent Systems (SCIS) on the individual right of privacy is discussed in this chapter under the light of the relevant privacy principles of the European Data Protection Legal Framework and the OECD Privacy Guidelines. This chapter analyzes the impact and limits of profiling, provenance and reputation on the right of privacy and review the legal privacy protection for profiles. From the technical perspective, we discuss opportunities and challenges for designing privacy-preserving systems for SCIS concerning collectives and decentralized systems. Furthermore, we present a selection of privacy-enhancing technologies that are relevant for SCIS including anonymous credentials, transparency-enhancing tools and the PrimeLife Policy Language (PPL) and discuss how these technologies can help to enforce the main legal principles of the European Data Protection Legal Framework.

Citation: Fischer-Hübner, S. and Martucci, L. A., “Privacy in Social Collective Intelligence Systems”, in Miorandi, D., Maltese, V., Rovatsos, M., Nijholt., A. and Stewart, J. (eds) Social collective intelligence: Combining the powers of humans and machines Springer, 2014.

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Introduction: Worldwide, societies are seeing rapid change in modes of social interaction and organization. These new interaction modes are predicated on emerging forms of information infrastructure together with rapidly evolving devices, systems, and applications that are ever more deeply interwoven with our social fabric. Computers and humans complement each other, as there are activities that are difficult or impossible for one and easy for the other. We argue that the key to activating the potential for the new generation of high-impact technologies and services aimed at enhancing human problem-solving capabilities lies in realizing a tight symbiosis between human and computers. In this symbiosis, information and communications technology (ICT) becomes an unobtrusive, pervasive extension of individuals and groups, empowering and enabling them to achieve ambitious objectives. We believe that a new form of intelligence can be devised to tackle complex and challenging problems, by exploiting,in a coordinated and holistic fashion, the complementary strengths of human and machines through the integration of computers, the Internet, individuals, and society. We term this integration “social collective intelligence” (SCI).

Citation: “Programming” Social Collective Intelligence. Daniele Miorandi and Lorenzo Maggi. In IEEE Technology and Society, special issue on Technology for Collective Action (2014).

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Executive Summary. This report first situates and outlines the potential of social computation to provide the basis for Social Collective Intelligence (SCI) in future systems. This involves the close interaction of social groups and machines together with systems of incentives and social structures to perform tasks that would otherwise be difficult to achieve either using entirely human or entirely machine solutions. The deliverable considers the challenges both from a technical and from a social science standpoint, identifying the potential for aligning them in order to provide an interdisciplinary perspective on the development of SCI systems. The paper then describes some of the challenges in developing an engineering approach to the development of such systems. Finally the paper outlines some of the “big questions” that arise from the framework for SCI research developed in the white paper.

Citation: Robertson, D., Anderson, S., Carreras, I., Miorandi, D., “D2.1 White Paper on Research Challenges in Social Collective Intelligence WP2 – Research Challenges and Strategic Analysis”, Social-IST (2013).

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Executive Summary. This document includes the outcomes of the activities carried out by the Social-IST Consortium on the identification and analysis of the application areas for which R&D&I initiatives on Social Collective Intelligence (SCI) can have a major impact. This serves as the basis for defining a set of recommendations and a possible roadmap to be taken into consideration when drafting future FET initiatives in the field of Social Collective Intelligence.

In a preliminary phase the Consortium identified, through desktop search, six relevant application areas for SCI, namely the Future of Work, the Future of Learning, Mobility and Transport in Cities of the Future, Healthcare and Well Being, Smart Energy and the Future of Science and Innovation. These areas have been analysed and discussed in details, in particular by means of (i) the two workshops held with the Social-IST Scientific Panel experts (ii) a Web survey open to the research community at large (iii) the final project event held in Oct. 2013. For each area, a number of scenarios were elaborated, leading to the identification of impacts on science, technology and society and of emerging research challenges.

The results of this analysis have been used for defining a roadmap for future EU initiatives in the field of SCI. This included (i) a proposal in terms of research methodology for running SCI projects and initiatives, (ii) a taxonomy of the most relevant research communities (iii) a mapping to the Horizon2020 Work programme and related calls.

This document is expected to provide some key insights on how to potentially exploit a Social Collective Intelligence approach in future calls and EU initiatives.

Citation: Carreras, I., Anderson, S., Robertson, D., Miorandi, D., “D3.1 Roadmap for FET Initiatives in Social Collective Intelligence, WP3 – High Impact Application Areas and Roadmapping”, Social-IST (2013).

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