Page - 49 - in Critical Issues in Science, Technology and Society Studies - Conference Proceedings of the 17th STS Conference Graz 2018

technology, quantification increasingly assumes the form of an immense datafication. “Big data” and the “data deluge” are looming over an increasing number of segments of modern society. We believe that a conceptual understanding of algorithmic regulation could make a contribution here. Another crucial point of reference is the literature on regulation. While a large part of regulatory studies has pursued a “command and control” (Black 2002) view of regulation with a strong focus on the state and its legal tools, more recently a part of the literature has turned towards a wider and more decentred understanding of regulation. Furthermore, notions such as “code is law” (Lessig 2006) or “regulation by design” (Yeung 2008) have disclosed the role of technology in regulation. These analyses provide a valuable starting point, but especially regarding the technological dimension of regulation a lot of work still remains to be done. Finally, science, technology and society studies provide our primary access point to understanding the interaction between technological artefacts and social processes. Bruno Latour and others have proposed a shift towards a “sociology of associations” (Latour 2005) that cultivates an interest in the complex entanglements and assemblings of human and non-human actants. It is in such a way that we are enabled to study the myriad of small changes that add up to the rise of new digital forms of regulation and regulatory regimes. Bridging the three literatures promises a fruitful and original perspective from which digitalization becomes visible as a non-linear, socially constructed and inherently political process. In the following we will sketch a conceptual framework that can guide such research. A framework for studying algorithmic regulation Our starting point for the analysis of regulation is a classic framework from regulatory studies, first spelled out by Hood, Rothstein and Baldwin (2001). This perspective adopts a cybernetic angle by analyzing regulation along the three components of information gathering, standard setting and behaviour modification. Karen Yeung (2017a) has drawn on this approach in her recent analysis of what she, adopting the term from O’Reilly (2013), refers to as algorithmic regulation. Her systematization is a valuable step towards a conceptual framework that we would like to build on by extending it with a number of further analytical distinctions. The first component is that of information gathering, which is any form of collecting knowledge about some segment of reality that is to be regulated. It involves an epistemic process of construction based on information as well as their interpretation and modelling (Mahr 2003). The increasing datafication of both processes has led to the current duality of big data and machine learning. Karen Yeung has distinguished between reactive and preemptive information gathering, where the former uses given data to detect violations and the latter uses it to predict future behaviour (Yeung 2017a). For a further analysis, Bruno Latour’s notion of “immutable mobiles” provides a fruitful starting point. Immutable mobiles for him are inscriptions that are “mobile but also immutable, presentable, readable and combinable with one another” (Latour 1990a) and thereby allow control at a distance. In an iconic way, this is illustrated by the map that makes it possible to control the territory. With Latour we can analyze modern digital information systems as an environment for new and more effective forms of immutable mobiles, which crucially rely on what Adrian MacKenzie, in his ethnography of machine learning, has called “vectorization”: the “drawing together” of a variety of heterogeneous aspects of reality into a mathematically well described feature space in which 49