It is precisely at such limits – between knowing and not-knowing, between the perceptible and the undetectable secrecy and revelation, between the rational and the absurd – that Trevor Paglen has persistently explored over the course of his artistic career (Bryan-Wilson, 2018, p.40).
Trevor Paglen describes himself as an ‘experimental geographer’. For those working in media and communication, you have probably heard of Trevor Paglen through his work with Kate Crawford. In these collaborations he is often referred to as an artist – and no doubt his work is deeply aesthetic. However, in many respects the description of ‘experimental geographer’ is more apt. Experimental geography looks at the way humans interact with their environment. It traverses the discourses of art, the military, journalism, geography and software with a focus on how institutions and the marketplace shape places, spaces and interactions. In this blog post I explore the work of Trevor Paglen and how the underlying principles of his work might be translated to the task of materialising data.
Much of Paglen’s work has focused on revealing the negative spaces, voids, holes and silences that appear empty. For example, in Code Names of the Surveillance State (2015) Paglen projects code words from more than 4000 National Security Agency (NSA) and Government Communications Headquarters (GCHQ) onto public buildings in an endless video loop. In Expeditions (2004-2008) Paglen documented restricted airfields and military bases while on a twice yearly camping trip. Paglen’s work documents how abstraction, mystification and secrecy is integral to economic and social processes. This clearly has links to how we might conceptualise data – there is a reason why data and data processing are obscured from view and it’s not just to do with efficiency and storage. In this way, what might initially appear ’empty’ instead ‘end up signifying quite loudly’ (Bryan-Wilson, 2018, p.46)
More recently, Paglen has been investigating the transformation of visual culture – from the fleshy and material (eyes, oils, gelatin and canvases) to the machinic (facial models, neural networks and mathematical abstraction). As he explains, a large part of ‘visual culture has become detached from human eyes and has become largely invisible’ (Paglen, 2019, p.24). Now images are created in order to be read by other machines – they are not made for human eyes (See blog post on post-representational logics). Think for example of the ubiquity of facial recognition technology – in our schools, cities and offices. Interestingly, Paglen points out, machines learn to ‘read’ these images by drawing on aspects of art theory, such as image segmentation and edge detection. Yet how features of the image are categorised and labelled reveals the socio-economic, cultural and racial backgrounds of the human developers working on these sets, rather than any kind of pluralistic version of society.
So how does Paglen’s work help in the quest to better understand and materialise data? First, Paglen’s work shows the importance of materializing hidden infrastructures, which is particularly relevant when thinking about data and it’s inherent obscurity and ambiguity. Second, his work highlights the fact that we do not need direct representations of phenomena to open up critical understandings. In fact, Paglen argues that the enterprise of enhanced vision – so much a feature of contemporary visual culture – might be productively countered by strategic incoherence and decomposition. Here there is a loop back to deconstructionism and the fact that any ‘text’ can have multiple, contradictory meanings that are complex and unstable. Paglen’s work shows these infrastructures – be they military bases, training images, or mathematical images – as decontextualized and disrupted. Decomposing an object or stripping it of a seamless, shiny veneer reveals much of its composition, as well as the ideas and assumptions that are baked into it.
Finally, Paglen’s work highlights the fact that developing a political consciousness of these infrastructures requires an understanding of the system to which they are a part. As he concludes his 2019 essay, the challenge of machinic vision, and many other systems is that we need to re-learn how to see, we cannot rely on the classic theories of old. That means ‘looking at the economies and market forces, or the political and legal forces, that they’re deployed to be a part of, and trying to understand how they actively sculpt the world rather than just reflect it or make representations of it’ (Paglen in Connell et al., 2018, p.35).
Image: Trevor Paglen, 2017, A rendering of “Sight Machine”
Bryan-Wilson, J. (2018). Survey: Trevor Paglen at the limit. Trevor Paglen. L. Cornell, J. Bryan-Wilson and O. Kholeif. London and New York, Phaidon.
Cornell, L., et al. (2018). Trevor Paglen. London and New York, Phaidon.
Paglen, T. (2018). “Invisible images: Your pictures are looking at you.” Architectural Design89(1): 22-27.