SOCIAM - Social Machines - will research into pioneering methods of supporting purposeful human interaction on the World Wide Web, of the kind exemplified by phenomena such as Wikipedia and Galaxy Zoo. These collaborations are empowering, as communities identify and solve their own problems, harnessing their commitment, local knowledge and embedded skills, without having to rely on remote experts or governments. Such interaction is characterised by a new kind of emergent, collective problem solving, in which we see (i) problems solved by very large scale human participation via the Web, (ii) access to, or the ability to generate, large amounts of relevant data using open data standards, (iii) confidence in the quality of the data and (iv) intuitive interfaces. "Machines" used to be programmed by programmers and used by users. The Web, and the massive participation in it, has dissolved this boundary: we now see configurations of people interacting with content and each other, typified by social web sites. Rather than dividing between the human and machine parts of the collaboration (as computer science has traditionally done), we should draw a line around them and treat each such assembly as a machine in its own right comprising digital and human components - a Social Machine. This crucial transition in thinking acknowledges the reality of today's sociotechnical systems. This view is of an ecosystem not of humans and computers but of co-evolving Social Machines. The ambition of SOCIAM is to enable us to build social machines that solve the routine tasks of daily life as well as the emergencies. Its aim is to develop the theory and practice so that we can create the next generation of decentralised, data intensive, social machines. Understanding the attributes of the current generation of successful social machines will help us build the next. The research undertakes four necessary tasks. First, we need to discover how social computing can emerge given that society has to undertake much of the burden of identifying problems, designing solutions and dealing with the complexity of the problem solving. Online scaleable algorithms need to be put to the service of the users. This leads us to the second task, providing seamless access to a Web of Data including user generated data. Third, we need to understand how to make social machines accountable and to build the trust essential to their operation. Fourth, we need to design the interactions between all elements of social machines: between machine and human, between humans mediated by machines, and between machines, humans and the data they use and generate. SOCIAM's work will be empirically grounded by a Social Machines Observatory to track, monitor and classify existing social machines and new ones as they evolve, and act as an early warning facility for disruptive new social machines. These lines of interlinked research will initially be tested and evaluated in the context of real-world applications in health, transport, policing and the drive towards open data cities (where all public data across an urban area is linked together) in collaboration with SOCIAM's partners. Putting research ideas into the field to encounter unvarnished reality provides a check as to their utility and durability. For example the Open City application will seek to harness citywide participation in shared problems (e.g. with health, transport and policing) exploiting common open data resources. SOCIAM will undertake a breadth of integrated research, engaging with real application contexts, including the use of our observatory for longitudinal studies, to provide cutting edge theory and practice for social computation and social machines. It will support fundamental research; the creation of a multidisciplinary team; collaboration with industry and government in realization of the research; promote growth and innovation - most importantly - impact in changing the direction of ICT.

SOCIAM - Social Machines - will research into pioneering methods of supporting purposeful human interaction on the World Wide Web, of the kind exemplified by phenomena such as Wikipedia and Galaxy Zoo. These collaborations are empowering, as communities identify and solve their own problems, harnessing their commitment, local knowledge and embedded skills, without having to rely on remote experts or governments. Such interaction is characterised by a new kind of emergent, collective problem solving, in which we see (i) problems solved by very large scale human participation via the Web, (ii) access to, or the ability to generate, large amounts of relevant data using open data standards, (iii) confidence in the quality of the data and (iv) intuitive interfaces. "Machines" used to be programmed by programmers and used by users. The Web, and the massive participation in it, has dissolved this boundary: we now see configurations of people interacting with content and each other, typified by social web sites. Rather than dividing between the human and machine parts of the collaboration (as computer science has traditionally done), we should draw a line around them and treat each such assembly as a machine in its own right comprising digital and human components - a Social Machine. This crucial transition in thinking acknowledges the reality of today's sociotechnical systems. This view is of an ecosystem not of humans and computers but of co-evolving Social Machines. The ambition of SOCIAM is to enable us to build social machines that solve the routine tasks of daily life as well as the emergencies. Its aim is to develop the theory and practice so that we can create the next generation of decentralised, data intensive, social machines. Understanding the attributes of the current generation of successful social machines will help us build the next. The research undertakes four necessary tasks. First, we need to discover how social computing can emerge given that society has to undertake much of the burden of identifying problems, designing solutions and dealing with the complexity of the problem solving. Online scaleable algorithms need to be put to the service of the users. This leads us to the second task, providing seamless access to a Web of Data including user generated data. Third, we need to understand how to make social machines accountable and to build the trust essential to their operation. Fourth, we need to design the interactions between all elements of social machines: between machine and human, between humans mediated by machines, and between machines, humans and the data they use and generate. SOCIAM's work will be empirically grounded by a Social Machines Observatory to track, monitor and classify existing social machines and new ones as they evolve, and act as an early warning facility for disruptive new social machines. These lines of interlinked research will initially be tested and evaluated in the context of real-world applications in health, transport, policing and the drive towards open data cities (where all public data across an urban area is linked together) in collaboration with SOCIAM's partners. Putting research ideas into the field to encounter unvarnished reality provides a check as to their utility and durability. For example the Open City application will seek to harness citywide participation in shared problems (e.g. with health, transport and policing) exploiting common open data resources. SOCIAM will undertake a breadth of integrated research, engaging with real application contexts, including the use of our observatory for longitudinal studies, to provide cutting edge theory and practice for social computation and social machines. It will support fundamental research; the creation of a multidisciplinary team; collaboration with industry and government in realization of the research; promote growth and innovation - most importantly - impact in changing the direction of ICT.

Despite being asked to "agree" constantly to terms of service, we do not currently have "meaningful consent." It is unclear whether having simple and meaningful consent mechanisms would change business fundamentally or enhance new kinds of economics around personal data sharing. Since consent is deemed necessary and part of a social contract for fairness, however, without meaningful consent, that social contract is effectively broken and the best intent of our laws undermined. Our research challenges to address this gap are interdisciplinary: meaningful consent has implications for transforming current digital economy data practices; change will require potentially new business models, and certainly new forms of interaction to highlight policy without over burdening citizens as we go about our business. We have set out a vision to achieve an understanding of meaningful consent through a combination of interdisciplinary expert and citizen activities to deliver useful policy, business and technology guidelines.