Polizeibehörden stehen vor einem Dilemma: Ihre IT-Strukturen sind komplex und über Jahre gewachsen, gleichzeitig wächst der Druck, Daten schneller und umfassender auszuwerten. Plattformen wie Palantir bieten dafür eine fertige Lösung und sind gerade deshalb attraktiv. Doch mit der schnellen Modernisierung können neue Abhängigkeiten entstehen: von einem Anbieter, seiner Technologie und den Strukturen, die sich um sie herum entwickeln.

This chapter focuses on one of the latest technical developments (not only) in police work: the emergence of data integration and analysis platforms. The police use these platforms to seamlessly combine data from different sources and to make it easier to analyze. These platforms are updating the myth that lots of data promises relevant findings and are also reproducing an association-centered logic of knowledge production. This is closely linked to the risk of increased surveillance.

Hopes and fears about algorithmic predictions are often rooted in the assumption that they represent a particularly actionable form of knowledge. Algorithmic predictions, the story goes, turn historical data into anticipatory actions instantly and on a large scale. Recent empirical evidence, however, casts doubt on whether algorithmic predictions are best understood as being inherently actionable. In this study, we draw on adjacent debates about actionable knowledge and reconceptualize the actionability of predictions as an active and deliberate construction accomplished by the designers of algorithmic systems. We demonstrate the value of this new perspective using material from an ethnographic research project on predictive policing in Germany. Over a 12-month period, we followed the work of designers in a police research unit responsible for developing an algorithmic system that generates crime predictions. We found that an important part of the designers’ work is calibrating the predictions. When engaging in calibration work, the designers adjust the form of the statistically calculated predictions—their volume, time, and space—to reflect their assumptions of what is most actionable knowledge for frontline police officers. Our study highlights a type of work on algorithmic systems that has received little attention, but which can have a significant impact on how the intended effects of algorithmic predictions are translated into their users’ actions.

Wir nehmen den 15. Jahrestag des Erscheinens von Goldsmiths (2010) Analyse zur neuen Sichtbarkeit der Polizei zum Anlass, seine Thesen aufzugreifen und einer erweiternden, visualitätssoziologisch informierten Analyse zu unterziehen. Wir argumentieren, dass der Annahme von Goldsmith, der zufolge polizeiliche Arbeit durch neue Videotechnologien stetig einer größeren gesellschaftlichen Sichtbarkeit unterworfen wird, zwar weiterhin bzw. noch stärker als zuvor zuzustimmen ist. Allerdings sind Erweiterungen seiner Argumentation angezeigt: Die neue Sichtbarkeit wirkt weniger direkt und auch weniger eindeutig als von ihm angenommen, sondern wird sowohl von polizeilichen Akteur:innen als auch von den betroffenen Bürger:innen und zivilgesellschaftlichen Beobachter:innen reflexiv aufgenommen und bearbeitet. Damit führt sie zu einer Veränderung von Praktiken und der Herausbildung neuer kommunikativer Formen. Wir diskutieren dies auf drei unterschiedlichen Ebenen – Situationen, strukturelle Veränderungen und reflexive Ausfomung –, die wir jeweils anhand eines empirischen Beispiels – polizeiliche Bodycams, Vernehmungen, Digitales Community Policing – plausibilisieren.

Abstract Although humans have long sought to produce the most accurate predictions possible about the future and apply them to make decisions in the present, recent developments in machine learning have led to predictions assuming an even more important role in society. There is hardly any societal field in which algorithmic forecasts are not carried out and hardly anyone who has not become the subject of forecasts in their life. Against this backdrop, this chapter presents the existing literature on the topic and reflects on how predictive analytics should be understood from a sociological perspective and on the consequences with which it may be associated. It will show that predictive analytics needs to be grasped as a socio-technical constellation because any genuine reference to the future does not result from the technical-analytical process itself but comes about only through human interpretation of the results as future oriented. In addition, the chapter discusses the human role in the collection of data, in the process of algorithmic model construction, and in the selection and creation of patterns. It also highlights the fact that predictions cannot be separated analytically from their final implementation, as this has important consequences that often follow a circular logic and can, ultimately, have discriminatory consequences.

The growing digitisation in our society also affects policing, which tends to make use of increasingly refined algorithmic tools based on abstract technologies. But the abstraction of technology, we argue, does not necessarily entail an increase in abstraction of police work. This paper contrasts the 'abstract police' debate with an analysis of police practices that use digital technologies to achieve greater precision. While the notion of abstract police assumes that computerisation distances police officers from their community, our empirical investigation of a geo-analysis unit in a German Land Office of Criminal Investigation shows that the adoption of abstract procedures does not by itself imply a detachment from local reference and community contact. What we call contextual reference can be productively combined with the impersonality and anonymity of algorithmic procedures, leading also to more effective and focused forms of collaboration with local entities. On the basis of our empirical results, we suggest a more nuanced understanding of the digitalisation of police work. Rather than leading to a progressive estrangement from the community of reference, the use of digital techniques can enable experimentation with innovative forms of 'precision policing', particularly in the field of crime prevention.

Organizations increasingly rely on digital technologies to perform tasks. To do so, they have to integrate data banks to make the data usable. We argue that there is a growing, academically underexplored market consisting of data integration and analysis platforms. We explain that, especially in the public sector, the regulatory implications of data integration and analysis must be studied because they affect vulnerable citizens and because it is not just a matter of state agencies overseeing technology companies but also of the state overseeing itself. We propose a platform-theory-based conceptual approach that directs our attention towards the specific characteristics of platforms—such as datafication, modularity, and multilaterality and the associated regulatory challenges. Due to a scarcity of empirical analyses about how public sector platforms are regulated, we undertake an in-depth case study of a data integration and analysis platform operated by Palantir Technologies in the German federal state of Hesse. Our analysis of the regulatory activities and conflicts uncovers many obstacles to effective platform regulation. Drawing on recent initiatives to improve intermediary liability, we ultimately point to additional paths for regulating public sector platforms. Our findings also highlight the importance of political factors in platform regulation-as-a-practice. We conclude that platform regulation in the public sector is not only about technology-specific regulation but also about general mechanisms of democratic control, such as the separation of power, public transparency, and civil rights.

In this paper, we conceptualize platforms that aim at data integration and analysis as a distinct type of digital platform. Based on the existing literature on digital platforms in general, which so far did not engage with data integration and analysis platforms, we present a definition of data integration and analysis platforms as a digital platform type in its own right. To emphasize the specificity of this platform type, we present a case example, Palantir Technologies, and highlight its structural characteristics and key technical features. The case study shows that data integration and analysis services should be understood as platforms, because like other platforms, they serve as modifiable digital infrastructures that bring together different parties and enable data-dependent interaction. It is especially important to understand data integration and analysis platforms as digital platforms because these platforms have their own politics and determine what happens on them, as we illustrate with regard to two important social values that are part of the data sovereignty of organizations: epistemic opacity and epistemic control. We conclude that it is time that platform research not only scrutinizes those technologies and companies that are visible to the eye of large numbers of end-users, but also those that operate in the dark.

This introduction to the special issue on data integration and analytics platforms provides an overview of the current importance of these platforms, with particular reference to the market leader, Palantir Technologies. In addition to the platformization of many areas of society through the use of data integration and analytics platforms, we argue that, where Palantir platforms are used, the process of 'Palantirization' can also be observed, because Palantir as a company is characterized not only by powerful platforms, but also by particular market entry techniques and, in particular, a decidedly political stance. This, we argue, is part of the package when Palantir's platforms were adopted.

Attempts to generate knowledge about the future and to make it usable for decisions in the present have existed for a long time in human history.Modern societies, however, are characterized by a particularly close relationship to the future and use numerous possibilities of (scientific) foreknowledge production to colonize it.Nonetheless, with recent advances in machine learning fuelled by predictive analytics, approaches to predicting the future in order to optimize strategies and actions in the present are becoming even more important.Before this backdrop, I analyse the application of predictive analytics as "prediction regimes," utilizing the Foucauldian governmentality approach.It is argued that predictive algorithms serve as "rendering devices," making the future calculable and, hence, governable in the present.

ZusammenfassungNachdem in den vorangegangenen Kapiteln auf sozialtheoretischer Ebene argumentiert, Diskurse als multimodale Topoi diskutiert und das Dispositiv als geeignetes Konzept für die Analyse ebendieser Multimodalität erörtert wurde, soll nun die Frage behandelt werden, wie sich diese Gedanken in eine theorie-empirische Dispositivanalyse des Drogentestens überführen lassen. Wie, mit anderen Worten, kann eine multimodale Dispositivanalyse methodologisch und methodisch umgesetzt werden?

Seit einigen Jahren ist in den Sozialwissenschaften – bisweilen unter dem Rubrum »material turn« subsumiert – eine verstärkte Diskussion um die gesellschaftliche Stellung von Materialität zu verzeichnen. Stets geht es um die adäquate Verhältnisbestimmung von menschlichen und nichtmenschlichen Partizipant:innen in sozialen Praktiken, die Aushandlung des Anspruchs post-anthropozentrischer Vorstellungen von Sozialität sowie die Konsequenzen, die sich aus derlei Positionen für die Diskurstheorie und -analyse ergeben. Entsprechende Diskussionen berühren auch die Diskursforschung, wird der »material turn« doch immer wieder explizit als (Abwehr-)Reaktion auf den »linguistic « bzw. » discursive turn« und mitunter zugleich als Fortführung poststrukturalistischer Ansätze positioniert. Das vorliegende Themenheft »Diskurs und Materialität« in der Zeitschrift für Diskursforschung will sich der Herausforderung annehmen, mithin das Potenzial der Diskursforschung für materialitätssensible Analysen aufzeigen.

Zusammenfassung Was Linde mit dem oben aufgeführten Zitat bereits vor einem halben Jahrhundert konstatierte, hat auch bis heute nicht an Gültigkeit verloren. Im Gegenteil: Aufgrund der zunehmenden Technisierung der Gesellschaft gilt es umso vehementer zu fordern, dass sich soziologische Analysen verstärkt mit den technischen und materialen Verhältnissen sozialer Interaktionen und Wissensbildungsprozessen auseinandersetzen. Dies gilt ebenfalls und gerade auch für die Diskurstheorie und -analyse, in deren Rahmen noch erheblicher Nachholbedarf mit Blick auf die technikgetriebene und materialbedingte Konstruktion diskursiven Wissens und dessen wirklichkeitskonstituierenden Effekte besteht.