Page - 50 - in Critical Issues in Science, Technology and Society Studies - Conference Proceedings of the 17th STS Conference Graz 2018

every concrete object can be represented as a vector (MacKenzie 2015). But every such vector is characterized by a complexity gap towards the reality it describes, which is why Susan Star concludes that no mobiles are completely immutable (Star 1995, 91). The local specificities undermine the modular and standardized descriptions of immutable mobiles. James Scott has developed the concept of “legibilization” (Scott 1998) to describe such simplifications aimed at control for the pre-digital world and we think it is highly relevant for the digital one as well. In order to analyze the strategic implications of such immutable mobiles as tools of legibilization, we propose to look at different kinds of algorithmic tools that can be employed in epistemic models for regulation. Examples would be simple deductive models along an IF-THEN logic, statistical models gained from training sets through supervised learning or mathematical optimization procedures that recommend analytically optimal behaviour. These tools, along with Slota’s and Bowker’s recent remark (2016) that knowledge is temporalized in new ways through algorithms, suggest that regulation might become more and more adaptive. The second, primarily normative component is that of standard setting. It refers to the goals that are to be obtained by regulation. Karen Yeung distinguishes between fixed and adaptive standards, i.e., those that are determined once and for all and those that can change over time (Yeung 2017a, 3). In order to become social realities, goals and standards of regulation undergo a process of definition, translation and calculation. No goal or standard ‘speaks for itself’, it needs indicators to be accessible and assessable. Goals and standards can take various forms: they can be shaped by a social domain or by a discipline, as in the case of the conflict between technical and legal standards; they can be area-specific, as in public health or commercial services; they can be abstract, like democratic values or customer satisfaction, or specific, like a 5% increase in users who are over 40 or a 1 pound weight loss within a month. The measurement can be quantitative or qualitative, direct or indirect, objective or subjective and absolute or relational. Whereas rules are always subject to interpretation due to the indeterminacy of language, code is binary and, even if not accurate, inherently determined. In order to be processed by algorithms, developers need to define their goals and standards in a way that can be applied to the data available. This requires the operationalization of broader goals that breaks them down into a variety of measurable entities. A specificity of algorithmic regulation is then the easy combination of more stable general goals and adaptive sub-standards. Behaviour modification, finally, encompasses the effective dimension of regulation: the actions through which a decision is enforced. It is the attempt to move the regulated entity closer towards a desirable state by deploying appropriate means. Yeung distinguishes between such systems that automatically administer specific sanctions or decisions and those that keep a human “on the loop” (ibid., 4), but while being useful, this distinction reveals little about how behaviour is actually modified. From the perspective of behaviour modification, regulation is successful to the extent that undesired forms of behaviour are rendered improbable. It is therefore dependent on a relation of strength and resistance or, in Latour’s words, on a relation between “programs” and “anti- programs” (Latour 1990b, 105ff.). “Programs” are those factors that contribute to steering the behaviour of a particular entity — be it human or not — in a direction deemed desirable. Modifying behaviour thus means mobilizing all available programs from which desired effects can be expected. “Anti-programs”, in contrast, refer to all the factors moving a particular entity in 50