Page - 54 - in Critical Issues in Science, Technology and Society Studies - Conference Proceedings of the 17th STS Conference Graz 2018

concerning gender identities on Facebook (Bivens 2017). In a third group of cases, users dispute the validity of inferred categories, for example the prediction of sexual orientation from Facebook posts. Standard setting is also a major site of contestation. Often, there are conflicts about the purposes that are being served and about the indicators against which success is measured. Many of debates also focus on the transparency and accountability of algorithmic decisions (Pasquale 2015; Ananny & Crawford 2018), for instance when people demand knowing how their creditworthiness is determined. When it comes to behaviour modification, conflicts arise about what instruments are legitimate and adequate. The current debates over the legitimacy of nudges (White 2013; Sunstein 2016) or about political microtargeting (Zuiderveen Borgesius et al. 2018) provide vivid examples. In essence, these are debates about whether certain forms of exerting influence can be reconciled with human dignity or democracy. The conflicts surrounding each of the three components also point towards what different forms of algorithmic regulation might look like. They show that there are other possible ways of gathering and processing information, other ways of defining goals, and other ways of influencing people. By criticizing the status quo, and by articulating other possibilities, the persons involved insist that there is a choice between different forms of establishing social order. In this sense, each of the three components, or all of them, can become politicized. Conclusion In this contribution, we have, in due brevity, sketched a framework for analysing regulation in different spheres of society. As an illustration of its applicability, we have shown cursorily how the regulation of drivers by Uber can be made sense of. Additionally, we have pointed towards other examples of information gathering, standard setting, and behaviour modification. We think that the framework presented above can make at least four contributions to science, technology and society studies. Firstly, it further differentiates the ways in which behaviour can be influenced through technology by taking into account a number of different theoretical angles. Secondly, focusing not only on influence, but also on data, models, and standards allows us to trace how different kinds of regulation are composed by establishing links between different building blocks. Thirdly, this framework helps us make sense of the digital transformation precisely because it is not limited to it; rather, we can compare digital and non-digital ways of regulation, and can thus determine what is and what isn’t unique about the digital transformation. Lastly, it allows us to distinguish various forms of political struggles around algorithmic regulation by directing scholarly attention to the regulatory components that are being politicized. Of course, the framework is no end in itself, but has to prove itself in empirical work. Future research will therefore be dedicated to refining it and using it to conduct comparative research. As a result of this, we hope to identify typical combinations of attributes across the three components. Such clusters would then allow a more detailed and contextualized understanding of algorithmic regulation and might point to strategies typical for specific spheres of society or levels of organization. Finally, such an analytical approach therefore promises a better informed diagnosis about how social order is established and maintained in the digital age. 54