What does it mean to be seen by machines?

Does a smile always mean we’re happy? For an emotion-tracking AI, it’s generally assumed so. Machine vision speaks a claustrophobic language of sameness. I have an acute awareness of the ways in which complex emotions are often simplistically reduced to outward appearances which is informed, at least in part, by my experiences with depression. When we’re talking about assumptions of what something should or shouldn’t look like, there are significant implications in terms disability politics, the policing of ableist ideals and discrimination against non-normative bodies. Machine vision technologies (such as emotion recognition and predictive policing¹) rely on establishing and reinforcing normative boundaries, and the effect is the shrinking of space for difference.

As an artist, much of my previous work has focused on surveillance, but it was in 2020 that I really honed in on the politics of machine vision technologies. I started developing an audiovisual work, Crystalline Unclear, and began to think about what it means to be seen by an AI trained on a dataset. At the same time, I was reading about crystallography and the process of using a beam of light (for example, an X-ray) to see the inner structure of a substance. I’ve heard it compared to producing a fingerprint of a material. In my work I focused on a piece of quartz which happens to be a crystal with a deep history in the development of electronics. Crystals are unique in their highly ordered, homogenous and predictable inner structures so if the beam of light reveals a structure that conforms to such a pattern, it’s easy to classify the crystal.

I began my research for Crystalline Unclear by thinking about machine vision and what it means to be seen by machines, but maybe the issue is more about what it means to be recognised by machines and the promotion of conformity that comes with it. Having said that, perhaps it still doesn’t go far enough to capture the scale of the issue, in that it doesn’t draw attention to the actions and consequences that follow as a result of recognition (and its inaccuracies). When recognition is used to compound or justify a police response at a protest, or the decisions of a driverless car heading for a crash, it goes beyond recognition and becomes about the chain of events that follows.

In many cases, it is arguably safer not to be seen or recognised by AI, a line of thinking also pursued by Francis Hunger & Flupke in their design of Adverserial.io, a webapp that subtly adds noise to images to prevent their machine-readability. When I was experimenting with it, a clear headshot of a person becomes wrongly pleasingly identified by the AI as a drumstick as a result of the added noise. To the human eye, however, the adjusted image and the original image are difficult to tell apart, demonstrating just how fragile and subject to manipulation machine vision can be.

So, what does it mean to be seen by machines? Perhaps it is not so different from being seen by a human, with the added caveat that biases and prejudices are more easily concealed and glossed over. What I hope that Crystalline Unclear can contribute to this conversation is an emphasis on the role of conformity, predictability, and the setting of norms to machine vision.

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¹ The use of predictive analytics in law enforcement to identify potential criminal activity.