Social AI are machine learning models used to create meaningful predictions and subsequent actions based on social media data and such models are becoming important engines of the data-driven society. Social AI automate what kind of news is being presented to what kind of people and the algorithms automate how people are depicted through social data. This AIAS project will critically scrutinize and discuss to what extent Social AI are able to create meaningful predictions that are sustainable both to our understanding of the social human being and to society. Through six case studies of empirical uses of different AI models, and a historical account of central AI problems, the project proposes a pragmatic theory of social AI. The emphasis is on contextualizing data as depictions of the human in which classifiers and training data and navigating conflicting ambiguity play central roles. The project proposes that making visible political dimensions of model training, reasoning and the connected interpretative work flows are together with a close eye for knowledge that can be derived from outliers, important steps that need to be taken in order to advance the further development of Social AI.
A Pragmatic Theory of Social AI
Area of research:
Algorithms and media sociology
1 Oct 2017 – 30 Sep 2018
Jens Christian Skou fellow
This fellowship has received funding from The Aarhus University Research Foundation.