Dialectics of training: A critique of recommendation engines’ aesthetic judgment

Abstract

In this article, we evaluate the politics of recommendation engines by focusing on an indispensible feature of their operation: training. We use the notion of training as a key word which helps us link three bodies of knowledge: data science, the history of automation, and aesthetic and political theory. Training is a staple in the operation of algorithmic systems, and artificial intelligence more generally; it is a practical methodology by which these systems become intelligent. Training is also a key feature of how workers throughout history came to perform their labor, and how, during the 20th century, machines came to acquire this human ability, that is, automation. And lastly, drawing on Immanuel Kant’s theory of aesthetic judgment, Hannah Arendt offers a political theory where training is key to political judgment. We trace the meaning and significance of ‘training’ in these three fields in order to draw conclusions from one field to another.