Continuities and discontinuities in the governance of motorized and autonomous traffic
Paper short abstract:
The paper will discuss continuities and discontinuities in the development of approaches to the technical governance of motorized and "smart" cars.
Paper long abstract:
The call states that data-based learning increasingly shapes social decision-making processes and that the algorithms used explicitly embody and implicitly transport sociotechnical norms and rule-sets. At the same time, existing sociotechnical practices and rule-sets are being changed through interaction with machine learning. At present, this is particularly observable in the field of self-driving cars and the intertwining of social learning and machine learning: "Society is learning about the technology while the technology learns about society" (Stilgoe 2017: 1). A crucial question is, how the ways of dealing with responsibility will be reconfigured. Against this background, a comparison shows how human responsibility, technology governance and legitimate decision-making have become established in the industrial phase of the incipient mechanization of automobile traffic, and to what extent today's approaches towards the governance of smart mobility differ from it. Based on an analysis of socio-technical imaginations and their current impact on decision-making in Germany my contribution will reconstruct astonishing continuities with, however, some significant differences. Stilgoe, J. (2017): Machine learning, social learning and the governance of self-driving cars. In: Social Studies of Science. Online first.
The power of correlation and the promises of auto-management. On the epistemological and societal dimension of data-based algorithms