SOCIAM: The Theory and Practice of Social Machines
These Social Machines are the focus of “SOCIAM - the theory and practice of Social Machines”. SOCIAM is a partnership between researchers from Southampton, Oxford and Edinburgh Universities who have embarked on a five-year (2012-2017) research programme. This project derives from concepts introduced by Tim Berners-Lee in his book Weaving the Web, where the Web is described as an engine to "create abstract social machines - new forms of social processes that would be given to the world at large”. You can find examples and information about the types of Social Machines that exist in the Social Machines section of the main SOCIAM website.
The ultimate ambition of SOCIAM is to enable us to understand how the Social Machines evolve in the wild and what factors influence their success and evolution. Its aim is to develop both theory and practice so that we can create the next generation of Social Machines.
The core objective of SOCIAM is to establish the research, methods, tools, networks and collaborations to allow us to understand social machines, in order that they can be designed and deployed by the full range of potential beneficiaries. The research is complex, as the 'components' of the social machine are both human and technological; the incentives for participation vary widely from personal gain to reciprocity to social responsibility to altruism, while problem identification and solution design are both radically decentralised.
Third party tracking in the mobile ecosystem
Reuben Binns‚ Ulrik Lyngs‚ Max van Kleek‚ Jun Zhao‚ Timothy Libert and Nigel Shadbolt
In Proceedings of the 10th International ACM Web Science Conference 2018. 2018.
'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions
Reuben Binns‚ Max Van Kleek‚ Michael Veale‚ Ulrik Lyngs‚ Jun Zhao and Nigel Shadbolt
In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 2018.
Measuring third party tracker power across web and mobile
Reuben Binns‚ Jun Zhao‚ Max Van Kleek and Nigel Shadbolt
In https://ora.ox.ac.uk/objects/uuid:86310ed1−762e−4037−a4d2−80568c5ee7c4. 2018.