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Trevor Paglen exhibition highlights how prejudice is tainting AI

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The epic expanse of photos in a new show at London’s Barbican Centre reveals a human side to artificial intelligence – and it isn’t pretty

Technology 9 October 2019

By Simon Ings

From…

IMAGES LOOK AT US / Trevor Paglen

The theoretical concepts we use to analyze classical visual culture are robust: representation, meaning, spectacle, semiosis, mimesis, etc. For centuries, these concepts have helped us to navigate the workings of classical visual culture... → But over the last decade or so, something dramatic has happened. Visual culture has changed form. It has become detached from human eyes and has largely become invisible

Human visual culture has become a special case of vision, an exception to the rule → The overwhelming majority of images are now made by machines for other machines, with humans rarely in the loop

Notion of the advent of machine-to-machine seeing → barely noticed at large, and poorly understood by those of us who’ve begun to notice the tectonic shift invisibly taking place before our very eyes

Images have begun to intervene in everyday life, their functions changing from representation and mediation, to activations, operations, and enforcement → Invisible images are actively watching us, poking and prodding, guiding our movements, inflicting pain and inducing pleasure...

We assume that humans are looking at images, and that the relationship between human viewers and images is the most important moment to analyze... but it’s exactly this assumption of a human subject that I want to question

What’s truly revolutionary about the advent of digital images → the fact that they are fundamentally machine-readable: they can only be seen by humans in special circumstances and for short periods of time (a photograph shot on a phone creates a machine-readable file that does not reflect light in such a way as to be perceptible to a human eye. A secondary application, like a software-based photo viewer paired with a liquid crystal display and backlight may create something that a human can look at, but the image only appears to human eyes temporarily before reverting back to its immaterial machine form when the phone is put away or the display is turned off)

However, the image doesn’t need to be turned into human-readable form in order for a machine to do something with it (fundamentally different than a roll of undeveloped film → film, too must be coaxed by a chemical process into a form visible by human eyes but the undeveloped film negative isn’t readable by a human or machine...)

The fact that digital images are fundamentally machine-readable regardless of a human subject → enormous implications → It allows for the automation of vision on an enormous scale and, along with it, the exercise of power on dramatically larger and smaller scales than have ever been possible...

Our built environments are filled with examples of machine-to-machine seeing apparatuses: 

Automatic License Plate Readers (ALPR) mounted on police cars, buildings, bridges, highways, and fleets of private vehicles snap photos of every car entering their frames

In the consumer sphere, Euclid Analytics and Real Eyes among many others install cameras in malls and department stores to track the motion of people through these spaces with software designed to identify who is looking at what for how long, and to track facial expressions to discern the mood and emotional state of the humans they’re observing

Advertisements have begun to watch and record people

In the industrial sector, companies like Microscan provide full-fledged imaging systems designed to flag defects in workmanship or materials, and to oversee packaging, shipping, logistics, and transportation for automotive, pharmaceutical, electronics, and packaging industries

All of systems above are only possible because digital images are machine-readable and do not require a human in the analytic loop...

This invisible visual culture isn’t just confined to industrial operations, law enforcement, and “smart” cities, but extends far into what we’d otherwise–and somewhat naively–think of as human-to-human visual culture...

On its surface, a platform like Facebook seems analogous to the musty glue-bound photo albums of postwar America → But the analogy is deeply misleading, because something completely different happens when you share a picture on Facebook than when you bore your neighbors with projected slide shows. When you put an image on Facebook or other social media, you’re feeding an array of immensely powerful artificial intelligence systems information about how to identify people and how to recognize places and objects, habits and preferences, race, class, and gender identifications, economic statuses, and much more.

Regardless of whether a human subject actually sees any of the 2 billion photographs uploaded daily to Facebook-controlled platforms → the photographs on social media are scrutinized by neural networks with a degree of attention that would make even the most steadfast art historian blush.

Facebook’s “DeepFace” algorithm → produces three-dimensional abstractions of individuals’ faces and uses a neural network that achieves over 97% accuracy at identifying individuals (a % comparable to what a human can achieve, ignoring for a second that no human can recall the faces of billions of people...)

If we take a peek into the internal workings of machine-vision systems, we find a menagerie of abstractions that seem completely alien to human perception

The machine-machine landscape → not one of representations so much as activations and operations → constituted by active, performative relations much more than classically representational ones. We might think of these synthetic activations and other “hallucinated” structures inside convolutional neural networks as being analogous to the archetypes of some sort of Jungian collective unconscious of artificial intelligence; a tempting (although misleading) metaphor

The point → if we want to understand the invisible world of machine-machine visual culture, we need to unlearn how to see like humans and need to learn how to see a parallel universe composed of activations, keypoints, eigenfaces, feature transforms, classifiers, training sets etc...

Not just as simple as learning a different vocabulary. Formal concepts contain epistemological assumptions, which in turn have ethical consequences → the theoretical concepts we use to analyze visual culture are profoundly misleading when applied to the machinic landscape, producing distortions, vast blind spots, and wild misinterpretations.

Ideology’s ultimate trick has always been to present itself as objective truth, to present historical conditions as eternal, and to present political formations as natural → Because image operations function on an invisible plane and are not dependent on a human seeing-subject (and are therefore not as obviously ideological as giant paintings of Napoleon) they are harder to recognize for what they are: immensely powerful levers of social regulation that serve specific race and class interests while presenting themselves as objective

The invisible world of images isn’t simply an alternative taxonomy of visuality → It is an active, cunning, exercise of power, one ideally suited to molecular police and market operations, one designed to insert its tendrils into ever-smaller slices of everyday life

“As governments seek out new sources of revenue in an era of downsizing, and as capital searches out new domains of everyday life to bring into its sphere, the ability to use automated imaging and sensing to extract wealth from smaller and smaller slices of everyday life is irresistible → It’s easy to imagine, for example, an AI algorithm on Facebook noticing an underage woman drinking beer in a photograph from a party. That information is sent to the woman’s auto insurance provider, who subscribes to a Facebook program designed to provide this kind of data to credit agencies, health insurers, advertisers, tax officials, and the police. Her auto insurance premium is adjusted accordingly. A second algorithm combs through her past looking for similar misbehavior that the parent company might profit from. In the classical world of human-human visual culture, the photograph responsible for so much trouble would have been consigned to a shoebox to collect dust and be forgotten. In the machine-machine visual landscape the photograph never goes away. It becomes an active participant in the modulations of her life, with long-term consequences.”

Smaller and smaller moments of human life are being transformed into capital → whether it’s the ability to automatically scan thousands of cars for outstanding court fees, or a moment of recklessness captured from a photograph uploaded to the Internet. Your health insurance will be modulated by the baby pictures your parents uploaded of you without your consent. The level of police scrutiny you receive will be guided by your “pattern of life” signature

Clear notion that the relationship between images and power in the machine-machine landscape is different than in the human visual landscape → The overall effect is a society that amplifies diversity (or rather a diversity of metadata signatures) but does so precisely because the differentiations in metadata signatures create inroads for the capitalization and policing of everyday life (GU)

Machine-machine systems are extraordinary intimate instruments of power that operate through an aesthetics and ideology of objectivity, but the categories they employ are designed to reify the forms of power that those systems are set up to serve. As such, the machine-machine landscape forms a kind of hyper-ideology that is especially pernicious precisely because it makes claims to objectivity and equality.

Cultural producers have developed very good tactics and strategies for making interventions into human-human visual culture in order to challenge inequality, racism, and injustice. These strategies rely on the fact that the relationship between meaning and representation is elastic → but this idea of ambiguity, a cornerstone of semiotic theory from Saussure through Derrida, simply ceases to exist on the plane of quantified machine-machine seeing. → There’s no obvious way to intervene in machine-machine systems using visual strategies developed from human-human culture

An effective resistance to the totalizing police and market powers exercised through machine vision won’t be mounted through ad hoc technology. In the long run, there’s no technical “fix” for the exacerbation of the political and economic inequalities that invisible visual culture is primed to encourage.

To mediate against the optimizations and predations of a machinic landscape, one must create deliberate inefficiencies and spheres of life removed from market and political predations,“safe houses” in the invisible digital sphere

We no longer look at images → images look at us. 

They no longer simply represent things → they actively intervene in everyday life. 

Trevor Paglen / transmediale 2014 keynote: Art as Evidence

Art as Evidence. A panel with Trevor Paglen, Jacob Appelbaum and Laura Poitras. Moderated by Tatiana Bazzichelli. Friday January 30, 2014, Haus der Kulturen …

Trevor Paglen, "Turnkey Tyranny, Surveillance and the Terror State"