We point the lens back

This image was created using the generative AI platform Midjourney, blended from a data set of surveillance images that I captured from a public square in Brighton, UK. The security camera that I used to capture the images for the data set was mounted on a public artwork that I produced. The camera was placed at human eye level, in full view - rather than being placed more discreetly, as security camera’s usually are. The surveillance footage depicted many people using their phones and cameras to photograph the artwork and the security camera. I fed this photographic data into Midjourney in order to find the average image (or in reference to Hito Steyerl’s account of machine learning outputs), the *mean* image. My reason for doing this was to understand what it looks like when we point the lens back at the cameras that surveil us. When the AI created images were generated from this data, I initially felt very uneasy about the results. The four images that were generated depicted men holding hybrid machines; cameras as weapons. I have chosen to share one of these images because it is the first AI generated work that I have made that punctures the usually very bland surface of this type of image making. I felt that I was onto something, as is as if the images were starting to reveal the violence of the contemporary camera from within its own system. Sontag (1977) explores the analogous connection between camera and gun (we shoot, we aim, we load), and the images generated offer an amalgam of this metaphor, exposing the mechanisms associated with ‘shooting’ in its many forms. In an age where increasingly, photographic technology is weaponised (from military contexts to algorithmic content), these images accidentally reveal the aggressive role of contemporary cameras and those that operate them.

I worry about a general lack of digital visual literacy within broader society, where images made using generative AI tools are read as photographs without any interrogation into how these images are created and who authors them. Of course we can argue that this concern has always existed (photography has always had the capacity to manipulate). However, the accessibility to software and the ease of making images with a photographic appearance has increased the likelihood for photographic images to be used in more sinister ways. The rise in pornographic deepfakes in the last few years for example, coincides with the development of generative tools. However, as a result of this significant shift, I feel hopeful that there is genuine critical discussion about digital literacy within educational contexts. It is perhaps the push that we needed to embed a critical approach to reading digital images and understanding their contexts in education for younger people.

Initially, it seems that artists are approaching generative tools with caution, aware that they may be probed about the excessive energy consumption that comes with each prompt, or the clear biases in the data sets that feed generative AI platforms. And artists should be cautious - machine learning tools rely on multiple levels of exploitation to function, from the underpaid workers exposed to harmful imagery when labelling data sets, to the communities affected by enormous data centres. To hack, subvert, or critique these processes then, feels like a necessary part of an artistic methodology when working with generative tools. I don’t think artists need to solely reference the technological infrastructures of the tools they are using, but the outcomes should at least aim to reflect on the broader role of this form of image-making within contemporary society, rather than attempting to use it as a purely representational tool. The relationship artists have with generative tools become more meaningful when they find out or reveal something about these tools and their impact on the world through using them.