ABSTRACT
This chapter deals with the impact of algorithms on publics. It engages with analyses of algorithms as an affect-based threat to publics. It discusses examples: The claim of the emergence of the so-called “filter bubbles” and discussions of Cambridge Analytica as an example of the manipulative potential of algorithms. In both cases, critics have overstated or wrongly analyzed the impact of algorithms on publics. Thus, the chapter engages with these forms of critique and argues that they tend to take over the specific view of subjects that inform the design and use of algorithms. This also leads to a particular reductive understanding of affect. The text argues that this way of conceiving both subjects and affects does not suffice to grasp the specific threat that algorithms pose for publics. A different understanding of subjects as members of publics and their susceptibility to affective influence is provided by using the works of Lauren Berlant on the influence of affects on political publics. Importantly, the text does not want to argue that there is no threat—just a different one to the one that is often discussed, that needs a different understanding of the affective relation between algorithms and publics.
