Research Associate Ariana Dongus published an essay for the SFKP, the Swiss Art Education Research eJournal of the Suisse “Fachgesellschaft für Kunstpädagogik.”
Read the full essay (in German) → here.
The essay deals with the development of machine vision which today has resulted in the automation of face and object recognition. The notion of automation suggests that no human input is needed and that the machine works on its own, in this case to execute the labour of perception. Complicating this popular notion, this text employs the concept of operative images by Harun Farocki as a point of departure. Farocki’s trilogy Eye/Machine I-III reflects the evolution of this new type of image: images from the factory, from research laboratories, surveillance cameras and images from the Iraq war. For Farocki, they are operative, i.e. directly effective: images that are actively embedded in processes as mathematical-technical operations. Importantly, the emergence of operative images points to new relations between machine and worker that will be discussed in this essay.
Farocki’s precise observations on this new image type are the starting point to confront techno-determinist and popular dystopian narratives of all-seeing surveillance scenarios with a differentiated materialist-feminist analysis, one that also takes economic changes into account. Several examples are used to show that artificial intelligence is in fact animated by global production networks of click workers. The manifold contributions of these workers remain invisible. Their platform work, from filtering out pornographic or violent content to annotating images for object recognition, is ghost work; precarious yet essential work that makes today’s software systems seem smart. The automation of perception, too often taken for machine autonomy, is revealed to be the result of a complex social relationship involving a planetary division of labor and collective intelligence of many workers.
Farocki described operative images at the beginning of the millennium as “bringing the work of recognition to the fore”. Today, almost two decades later, it means putting both the perceptional labor invested in teaching machines how to recognize and the network of workers who carry it out in the foreground. Artificial intelligence is intrinsically linked to this work. AI is not intelligent and does not function automatically, as through a magician’s slight of hand. Rather, making AI work lays in the hands of gig economy workers. The magician is a collective of workers and the pixels of the pictures are alive.