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Although lie detection procedures have been fundamentally criticized since their inception at the beginning of the 20th century, they are still in use around the world. In addition, they have created some remarkable appeal in the context of counterterrorism policies. Thereby, the links between science and fiction in this topic are quite tight and by no means arbitrary: In the progressive narrative of the lie detection devices, there is a promise of changing society for the better, which is entangled in a fictional narrative provided by many cinematic and literary examples. By drawing on the concept of "diegetic technologies" formulated by David Kirby, we want to highlight the role of science fictional narratives in the historical development and current application of lie detection procedures. We therefore aim to use this conceptual frame to analyze the role of prevention-oriented mind reading procedures, which are developed for or already used in the "war on terror" with reference to their fictional predecessors. One virulent factor has to be highlighted in this context: The fictional engagement with possible new lie detection practices is in itself creating a legitimating ground for future security technologies.

Digital prediction tools increasingly complement or replace other practices of coping with an uncertain future. The current COVID-19 pandemic, it seems, is further accelerating the spread of prediction. The prediction of the pandemic yields a pandemic of prediction. In this paper, we explore this dynamic, focusing on contagion models and their transmission back and forth between two domains of society: public health and public safety. We connect this movement with a fundamental duality in the prevention of contagion risk concerning the two sides of being-at-risk and being-a-risk. Both in the spread of a disease and in the spread of criminal behavior, a person at risk can be a risk to others and vice versa. Based on key examples, from this perspective we observe and interpret a circular movement in three phases. In the past, contagion models have moved from public health to public safety, as in the case of the Strategic Subject List used in the policing activity of the Chicago Police Department. In the present COVID-19 pandemic, the analytic tools of policing wander to the domain of public health – exemplary of this movement is the cooperation between the data infrastructure firm Palantir and the UK government's public health system NHS. The expectation that in the future the predictive capacities of digital contact tracing apps might spill over from public health to policing is currently shaping the development and use of tools such as the Corona-Warn-App in Germany. In all these cases, the challenge of pandemic governance lies in managing the connections and the exchanges between the two areas of public health and public safety while at the same time keeping the autonomy of each.

Patterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rearticulated and reinforced in police work. Based on empirical research about predictive policing software and practices, this article puts the authority of patterns into perspective. Introducing four ideal-typical styles of pattern identification, we illustrate that patterns are not based on a singular logic, but on varying rationalities that give form to and formalize different understandings about crime. Yet, patterns render such different modes of reasoning about crime, and the way in which they feed back into policing cultures, opaque. Ultimately, this invites a stronger reflection about the political nature of patterns.

Cover -- Table of Contents -- 1. Knowing in Algorithmic Regimes: An Introduction -- Juliane Jarke, Bianca Prietl, Simon Egbert, Yana Boeva, and Hendrik Heuer -- I. METHODS -- 2. Revisiting Transparency Efforts in Algorithmic Regimes -- Motahhare Eslami and Hendrik Heuer -- 3. Understanding and Analysing Science's Algorithmic Regimes: A Primer in Computational Science Code Studies -- Gabriele Gramelsberger, Daniel Wenz, and Dawid Kasprowicz -- 4. Sensitizing for Algorithms: Foregrounding Experience in the Interpretive Study of Algorithmic Regimes -- Elias Storms and Oscar Alvarado -- 5. Reassembling the Black Box of Machine Learning: Of Monsters and the Reversibility of Foldings -- Juliane Jarke and Hendrik Heuer -- 6. Commentary: Methods in Algorithmic Regimes -- Adrian Mackenzie -- II. INTERACTIONS -- 7. Buildings in the Algorithmic Regime: Infrastructuring Processes in Computational Design -- Yana Boeva and Cordula Kropp -- 8. The Organization in the Loop: Exploring Organizations as Complex Elements of Algorithmic Assemblages -- Stefanie Büchner, Henrik Dosdall, and Ioanna Constantiou -- 9. Algorithm-Driven Reconfigurations of Trust Regimes: An Analysis of the Potentiality of Fake News -- Jörn Wiengarn and Maike Arnold -- 10. Recommender Systems beyond the Filter Bubble: Algorithmic Media and the Fabrication of Publics -- Nikolaus Poechhacker, Marcus Burkhardt, and Jan-Hendrik Passoth -- 11. Commentary: Taking to Machines: Knowledge Production and Social Relations in the Age of Governance by Data Infrastructure -- Stefania Milan -- III. POLITICS -- 12. The Politics of Data Science: Institutionalizing Algorithmic Regimes of Knowledge Production -- Bianca Prietl and Stefanie Raible -- 13. Algorithmic Futures: Governmentality and Prediction Regimes -- Simon Egbert -- 14. Power and Resistance in the Twitter Bias Discourse -- Paola Lopez.