Safety in Numbers?
Um artigo publicado na Frieze, março 2014.
Algorithms, Big Data and surveillance: what’s the response, and responsibility, of art? Jörg Heiser asked seven artists, writers and academics to reflect.
Trevor Paglen, They Watch the Moon, 2010
Something fundamental is changing in the world of images, and in the landscape of seeing more generally. We are at the point (actually, probably long past) where the majority of the world’s images are made by-machines-for-machines. In this new age, robot-eyes, seeing-algorithms and imaging-machines are the rule, and seeing with the meat-eyes of our human bodies is increasingly the exception.
Machines-seeing-for-machines is a ubiquitous phenomenon, encompassing everything from infrared qr-code readers at supermarket check-outs to the Automatic Number Plate Recognition (ANPR) cameras on police cars and urban intersections; facial-recognition systems conduct automated biometric surveillance at airports, while department stores intercept customers’ mobile-phone pings, creating intricate maps of movements through the aisles. Beyond that, the archives of Facebook and Instagram hold hundreds of billions of photographs, which are trawled by sophisticated algorithms searching for clues about the behaviours and tastes of the people and scenes depicted in them. But all of this seeing, all of these images, are essentially invisible to human eyes. These images aren’t meant for us: they’re meant to do things in the world; human eyes aren’t in the loop.
All of this is new. Although Guy Debord’s spectacle society has certainly not gone anywhere, the advent of ‘operationalized’ images is upon us. The 21st-century landscape of images and seeing-machines directly intervenes in the surrounding world. Seeing-machines do things-in-the-world not through the subtle ideologies of visual mythmaking and fetishism, but through quantification, tracking, targeting and prediction.
How do we begin to think about the implications on societies at large of this world of machine-seeing and invisible images? Conventional visual theory is useless to an understanding of machine-seeing and its unseen image-landscapes. As for art, I don’t quite know, but I have a feeling that those of us who are interested in visual literacy will need to spend some time learning and thinking about how machines see images through unhuman eyes, and train ourselves to see like them. To do this, we will probably have to leave our human eyes behind. A paradox ensues: for those of us still trying to see with our meat-eyes, art works inhabiting the world of machine-seeing might not look like anything at all.
Trevor Paglen is an artist.