Page - 53 - in Critical Issues in Science, Technology and Society Studies - Conference Proceedings of the 17th STS Conference Graz 2018

drivers go in the right direction, which is essential for an enterprise specialized in transportation. Hence, local knowledge of streets and routes is not required for drivers. Preferably, drivers go along an Uber-chosen route. However, the criteria for suggesting a given route are not disclosed to the driver. It could be the shortest route or the route with maximum (or minimum) public visibility of the Uber car itself. 4) Fourth and last, sophisticated influence is exerted by exploiting the non-rational characteristics of drivers. For instance, Uber exploits people’s “loss aversion“, a phenomenon well-documented in behavioural economics, in a specific way: When drivers are about to log off, they receive a message reminding them of the money they would ‘lose’ by stopping now. For instance, one message reads “Are you sure you want to go offline? Demand is very high in your area. Make more money, don’t stop now!“ (Rosenblat & Stark 2016, 3768). Further applications The framework presented here is not limited to investigating the case of Uber. It can also shed light on other cases in which technological artefacts are used for regulation. On the micro-level, we can analyse forms of technologically mediated self-regulation. Fitbits and other kinds of wearable fitness trackers which are supposed to improve their users’ health are striking examples. Sleep trackers like WakeMate or energy use trackers like Wattvision enable similar kinds of self-regulation. A conceptual framework of regulation can add a fruitful perspective to the ongoing debate about the “quantified self” (Lupton 2016). On the meso-level, we can analyse organizational regulation that is supported by artefacts or software. For instance, Raffetseder, Schaupp and Staab (2017) have investigated the software Salesforce that is able to automatically assign incoming tasks to employees, according to parameters such as current workload or experience. And recently, Amazon developed a wristband that vibrates when employees in warehouses place goods in the wrong shelves (Yeginsu 2018) – a form of excorporate behaviour modification that affords itself to quick incorporation. Our framework also allows us to analyse how whole populations or sub-populations are regulated with the help of technology. Examples are computer-assisted forms of organizing the criminal justice system (such as the ambitions of predictive policing products like PredPol or sentencing software like Northpointe’s COMPAS), the automated curation of media content practiced by Facebook through its Newsfeed algorithm or the spectacular vision of a unified citizen score that currently haunts the newspapers with regards to the plans and actions of the Chinese government. Politicization Another advantage of this framework is that it sheds light not only on the ways in which algorithmic regulation operates, but also on the ways in which it is contested and politicized. Many of the conflicts around algorithmic regulation can be attributed to one of the three components. Some conflicts mainly focus on the gathering and the modeling of data. For instance, many privacy debates centre on the question who has the right to use which information, or which categories of information are really necessary for providing a service (for the case of Uber, see Zakrzewski 2015). In other cases, users request not less but more categories, for example 53