e. If the number of skin regions is more than 60 and the average intensity within the polygon is less than 0.25, the image is not nude.
f. Otherwise, the image is nude.8
But this method got ridiculed pretty quickly because it produced so many false positives, including, as in some examples, wrapped meatballs, tanks, or machine guns. More recent porn-detection applications use self-learning technology based on neural networks, computational verb theory, and cognitive computation. They do not try to statistically guess at the image, but rather try to understand it by identifying objects through their relations.9
According to developer Tao Yang’s description, there is a whole new field of cognitive vision studies based on quantifying cognition as such, on making it measurable and computable.10 Even though there are still considerable technological difficulties, this effort represents a whole new level of formalization; a new order of images, a grammar of images, an algorithmic system of sexuality, surveillance, productivity, reputation, and computation that links with the grammatization of social relations by corporations and governments.
So how does this work? Yang’s porn-detection system must learn how to recognize objectionable parts by seeing a sizable mass of them in order to infer their relations. So basically you start by installing a lot of photos of the body parts you want eliminated on your computer. The database consists of folders full of body parts ready to enter formal relations. Not only pussy, nipple, asshole, and blowjob, but asshole, asshole/only and asshole/mixed_with_pussy. Based on this library, a whole range of detectors get ready to go to work: the breast detector, pussy detector, pubic hair detector, cunnilingus detector, blowjob detector, asshole detector, hand-touch-pussy detector. They identify fascinating sex-positions such as the Yawning and Octopus techniques, The Stopperage, Chambers Fuck, Fraser MacKenzie, Persuading of the Debtor, Playing of Cello, and Watching the Game (I am honestly terrified of even imagining Fraser MacKenzie).
This grammar as well as the library of partial objects are reminiscent of Roland Barthes’s notion of a “porn grammar,” where he describes the Marquis de Sade’s writings as a system of positions and body parts ready to permutate into every possible combination.11 Yet this marginalized and openly persecuted system could be seen as a reflex of a more general grammar of knowledge deployed during the so-called Enlightenment. Michel Foucault as well as Theodor W. Adorno and Max Horkheimer compared de Sade’s sexual systems to mainstream systems of classification. Both were articulated by counting and sorting, by creating exhaustive, pedantic, and tedious taxonomies. And Mr. Yang’s enthusiasm for formalizing body parts and their relations to one another similarly reflects the huge endeavor of rendering cognition, imaging, and behavior as such increasingly quantifiable and commensurable to a system of exchange value based in data.
Undesirable body parts thus become elements of a new machine-readable image-based grammar that might usually operate in parallel to reputational and control networks, but that can also be linked to it at any time. Its structure might be a reflex of contemporary modes of harvesting, aggregating, and financializing data-based “knowledge” churned out by a cacophony of partly social algorithms embedded into technology.
Noise and Information
But let’s come back to the question we began with: What are the social and political algorithms that clear noise from information? The emphasis, again, is on politics not algorithms. Jacques Rancière has beautifully shown that this division corresponds to a much older social formula: to distinguish between noise and speech in order to divide a crowd between citizens and rabble.12 If one wants not to take someone else seriously, or to limit their rights and status, one pretends that their speech is just noise, garbled groaning, or crying, and that they themselves must be devoid of reason and therefore exempt from being subjects, let alone holders of rights. In other words, this politics rests on an act of conscious decoding —separating “noise” from “information,” “speech” from “groan,” or “face” from “butt,” and from there neatly stacking the results into vertical class hierarchies.13 The algorithms now being fed into smartphone camera technology to define the image prior to its emergence are similar to this.
In light of Rancière’s proposition, we might still be dealing with a more traditional idea of politics as representation.14 If everyone is aurally (or visually) represented, and no one is discounted as noise, then equality might draw nearer. But the networks have changed so drastically that nearly every parameter of representative politics has shifted. By now, more people than ever are able to upload an almost unlimited number of self-representations. And the level of political participation by way of parliamentary democracy seems to have dwindled in the meantime. While pictures float in numbers, elites are shrinking and centralizing power.
On top of this, your face is getting disconnected—not only from your butt, but also from your voice and body. Your face is now an element—a face/mixed_with_phone, ready to be combined with any other item in the library. Captions are added, or textures, if needs be. Face prints are taken. An image becomes less a representation than a proxy, a mercenary of appearance, a floating texture-surface-commodity. Persons are montaged, dubbed, assembled, incorporated. Humans and things intermingle in ever-newer constellations to become bots or cyborgs.15 As humans feed affect, thought, and sociality into algorithms, algorithms feed back into what used to be called subjectivity. This shift is what has given way to a post-representational politics adrift within information space.16
Proxy Armies
Let’s look at one example of post-representational politics: political bot armies on Twitter. Twitter bots are bits of script that impersonate human activity on social media sites. In large, synchronized numbers they have become formidable political armies.17 A Twitter chat bot is an algorithm wearing a person’s face, a formula incorporated as animated spam. It is a scripted operation impersonating a human operation.
Bot armies distort discussions on Twitter hashtags by spamming them with advertisements, tourist pictures, or whatever. They basically add noise. Bot armies have been active in Mexico, Syria, Russia, and Turkey, where most political parties have been said to operate such bot armies. In Turkey, the ruling AKP alone was suspected of controlling 18,000 fake Twitter accounts using photos of Robbie Williams, Megan Fox, and other celebs: “In order to appear authentic, the accounts don’t just tweet out AKP hashtags; they also quote philosophers such as Thomas Hobbes and movies like PS: I Love You.”18
So who do bot armies represent, if anyone, and how do they do it? Let’s have a look at the AKP bots. Robbie Williams, Meg Fox, and Hakan43020638 are all advertising “Flappy Tayyip,” a cell-phone game starring the then AKP prime minister (now president) Tayyip Recep Erdoğan. The objective is to hijack or spam the hashtag #twitterturkey to protest PM Erdoğan’s banning of Twitter. Simultaneously, Erdoğan’s own Twitter bots set out to detourne the hashtag.
Let’s look at Hakan43020638 more closely: a bot consisting of a copy-pasted face plus product placement. It takes only a matter of minutes to connect his face to a body by way of a Google image search. On his business Twitter account it turns out he sells his underwear: he works online as an affective web service provider.19 Let’s call this version Murat, to throw yet another alias into the fray. But who is the bot wearing Murat’s face and who is a bot army representing? Why would Hakan43020638 be quoting Thomas Hobbes of all philosophers? And which book? Let’s guess he’s quoting from Hobbes’s most important work Leviathan. Leviathan is the name of a social contract enforced by an absolute sovereign in order to fend off the dangers presented by a “state of nature” in which humans prey upon one another. With Leviathan there are no more militias and there is no more molecular warfare of everyone against everyone.
But now we seem to be in a situation where state systems grounded in such social contracts seem to fall apart in many places and nothing is left but a set of policed relational metadata, emoji, and hijacked hashtags. A bot army is a contemporary vox populi, the voice of the people according to social networks. It can be a Facebook militia,