SOCIAM: The Theory and Practice of Social Machines

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.

The proposed programme will have beneficial impact on a wide range of stakeholders. Via technology transfer, companies will gain access to new technologues, and also gain the understanding that will allow them to develop new products for communities organising themselves in social machines. Those companies that partner us or support our research will of course have the ability to feed ideas into the research, and frame the problems we are trying to solve; we consider it essential that fundamental research feeds into, and back from, real-world applications.

Smaller-scale entrepreneurs will have new outlets for innovation, and new opportunities to develop radical business models. The public sector and third sector will have available new tools and methods for achieving policy ends. Communities using social machines will also benefit, of course, by the ability to identify and define their own problems, and develop their own solutions. These benefits, in social cohesion and cooperation, will often outlive the immediate issue which drove the development of the social machine.

We should not forget the benefits to the wider academic community of the proposed research. Of course, the development of a community of multi-disciplinary researchers in social machines will benefit the computer science field, but via the observatory and the strong social relevance of the research, we would expect a wide academic community in science and social science benefiting from the deepening of expertise in this area, and the large quantity of data. The 5-year programme would allow a strong multi-disciplinary cohort of researchers to emerge, able to influence a range of fields, spreading expertise in these relatively novel methods of social collaboration. Dissemination will also take place via our programme of Town Meetings, sandpits, hackathons, disruptive skills workshops, etc. Groups associated with the consortium, such as the Web Science Trust, will be able to ensure that SOCIAM's work is widely disseminated and one of our Partners is the world's largest Technical PR Agency.

The impacts will be both economic and non-economic. The economic impacts will be the benefits that come from innovation and cooperation, and from bottom-up solutions to problems. These will include both lowering costs of social problems (e.g. via community policing lowering the costs of crime), and creating opportunities for innovation and commercial exploitation of innovation (as for example with the development of new services based on creative uses of available data). Some of these benefits will fall to entrepreneurs, while others will spill over into the wider community.

Furthermore, the research will enable value to be extracted from the ever-growing quantities of data we see. The social return on investment in data acquisition, particularly public open data, will be dramatically improved as more tools and methods are created for using the data to drive services.

There will be several non-economic impacts too. In policy terms, the impacts will be high, particularly as local solutions for problems - inherently more efficient than centralised problem-solving which cannot always take account of local conditions - will emerge from collaboration in social machines in small communities. Communities will become empowered and self-reliant. The result will be a suite of tools and methods which can be put to work in social contexts by a range of actors - government, to achieve policy goals, groups of people, to achieve social goals, or entrepreneurs, to achieve commercial goals. Indeed, one would expect a social machine to encompass all of these at different times.