På AI-teknikens axlar: Om kunskapssociologin och stark artificiell intelligens

This dissertation is concerned with Sociology’s stance in the debate on Strong Artificial Intelligence, i.e. such AI that is able to shape new knowledge without human interference. There is a need for sociologists to realize the difference between two approaches to constructing AI systems: Symbolic AI (or Classic AI) and Distributed AI – DAI.
Sociological literature shows a largely critical attitude towards Symbolic AI, an attitude that is justified. The main theme of this dissertation is that DAI is not only compatible with Sociology’s approach to what is social, but also constitutes an apt model of how a social system functions. This is consolidated with help from Niklas Luhmann’s social systems theory and from Vygotsky-oriented education scientists who claim that processes leading to new knowledge are about expansion and not about problem solving. Problem solving only leads to elaborating existing knowledge. It is shown that human being has always used several types of artefacts and tools to produce culture and knowledge. This shows that humans are dependent on their environment and that knowledge is not only something that has to do with their brain, but also the rest of their bodies and their environments.
Further, Strong AI is not about robots thinking in the same way as humans, but about holistic social processes where the actor does not need to have a complex functionality. This provides a good opportunity to explain what sociologists call emergency, i.e. how social processes shape new knowledge independent of human actors.

The possibility of AI has to be evaluated in terms of human beings’ capacities to cope with reflexive processes. Luhmann teaches us that we have to see the difference between three different forms of reflexivity: self-reference, reflexivity and reflection. We contend that, in order to be able to shape new knowledge in expanding processes, there must be circumstances that allow reflection. Luhmann writes that knowledge-producing processes are dependent on capacities for reflection between the social systems and their environments. Strong AI has more capacity to handle these processes than humans have, while the strongest argument for strong DAI is that it can reach a far wider area than human beings’ brains can. This capacity for reflection is a better way of judging the capacity of strong AI, than questions about causal capacity and capacity for action.