How Christie’s so-called ‘AI-generated’ art sale proves documents can distort history (and other information)

Every Monday morning, artnet News brings you The Gray Market. The column decodes important stories from the previous week and offers unparalleled insight into the inner workings of the art industry in the process.

For this edition, three thoughts on the most mind-boggling story of the week…


On Thursday, a gold-framed canvas print by algorithm-powered French trio Obvious sold for $432,500, more than 4,320% off its high estimate of $10,000, once the buyer’s premium is included. . And while the sale raises several questions worthy of our attention, the most important to me is what it shows about the ability of the art market to shape historical narrative as it pleases.

About two months ago, Christie’s announced loud and clear to the traditional art world that this work, titled Portrait of Edmond de Belamy, is “the first AI-generated artwork to be auctioned”. However, this claim is dubious in two important ways, both of which rely on our willingness to accept Christie’s marketing at face value.

First, as Naomi Rea and I wrote last month, the portrayal of this work as “AI-generated” (or similar) is crude at best. It confuses humans powering labeled examples in what started as an open source algorithm with the big concepts of artificial development general intelligence or superintelligence, in which machines become sentient and goal-directed without human guidance.

There’s a vigorous debate raging right now about what credit Obvious should be able to claim for Portrait of Edmond de Belamy– and not just in terms of what is, in my view, the substantially misleading gesture of listing the algorithm as the “artist” of the impression. Since Naomi will soon have more to say on this angle, I’ll just point out that even understanding what happened requires unpacking a wave of technical jargon unknown to most, both inside and out. outside of the mainstream art world (including us at artnet News, who have been flawed thus far). And the learning curve opens up the potential for distortion, whether she knows it or not.

Based on what I’ve seen, I tend to believe that Christie’s itself falls into the “unknowingly” category. Head of Prints and Multiples Richard Lloyd, who recorded the portrait of its creators, admitted to the art diary that he “only heard of Obvious when he read” an article on artnet about a collector who had purchased one of their works “earlier this year”. To me, that’s about as reactionary as looking up from a fortune cookie promising true love and proposing to the first person you see in Panda Express, and that says a lot about how the top end of the market superficially engages in art and machine learning. at present.

But the terminology of the tech world isn’t the only misunderstanding here. There are at least as many biases that occur when it comes to the side of this story recognizable to virtually everyone in the mainstream art world: the notion of being “the first to come to auction”.

A Google Deep Dream painting inspired by Vincent van Gogh. Image courtesy of Google.


In February 2016, the Gray Area Foundation for the Arts in San Francisco raised $98,000 in an auction of works by 10 artists produced using DeepDream, Google’s open-source neural network. For the uninitiated, a neural network is the same type of software used by Obvious to create Portrait of Edmond de Belamy. So if we’re going to consider the work of the “AI-generated” French trio, then Gray Area has technically beaten Christie’s at auction for this genre by over two and a half years.

But when I raised this point with several people in the art business, they all responded with some variation of this thought, “Well, it was just a non-profit perk, not a sale in the art business. ‘one of the great houses.’

This response reveals an unspoken assumption that has crept into the consciousness of the art world: that “coming to auction” no longer refers to a general mode of public sale. Instead, it refers to a mode of public sale made by a market leading gatekeeper.

Most important, Why has this become the working definition of “coming to auction”? Because, on the whole, we have come to tacitly accept that the major market gatekeepers are the most important sources for tracing the history of the art market. And as the market becomes an increasingly powerful force in shaping public understanding, they also become (like it or not) the most important sources for charting the history of art itself. .

This phenomenon is not exclusive to the arts. If you allow me a sports analogy, Major League Baseball, the American professional league for the sport, called its championship round the World Series since 1903. Why has global success been defined by MLB standards for 115 years? Because it has always been the most visible, profitable and prestigious baseball league on the planet. In sport as in art as in so many other things, strength is good.

Is much of the mainstream art world now on track to ignore important machine learning artists like it has ignored so many important non-white, non-male artists in the past? Kerry James Marshall, Untitled (Painter) (2009). Courtesy of the Museum of Contemporary Art Chicago. Photo: Nathan Keay, ©MCA Chicago.


Now, in fairness, Christie’s has been more circumspect about the terms of some press materials than others – for example, calling itself (emphasis mine) the “first auction lodge» propose a work like Portrait of Edmond de Belamy, or describing the print as “the first AI-generated product portrait come to auction.

Yet seeing and appreciating fine distinctions like these requires legalistic attention to detail. They might matter if everyone in the art world also correctly pluralized the states’ top legal minds as “attorney generals” or used the Chicago Manual of Style to format their Instagram captions. But we don’t.

So what the art media and the art industry are communicating is the original and grand narrative, one that casts Christie’s and Obvious as the forerunners in a field where less visible artists pioneered for years.

LaTurbo Avedon, an artist whose practice explores the authorship of virtual space and other issues, tweeted an overly revealing thought a few minutes after the Obvious sale:

We in the art world are now in the midst of a great awakening of artists and practices unjustly dormant during the arc of the 20th century: female artists, LGBTQI artists, artists of African or Latin origin, artists in outside the western canon. Seemingly every week, someone in the mainstream art world makes the news with an action intended to correct what we now universally recognize as heinous oversights by the institutional and commercial sectors.

Taken together, the Obvious sale and the rhetoric surrounding it reveal something extremely distressing: even as we in the mainstream art world try to fix these mistakes, many of us are making the same kind of mistake again. Only this time, as Avedon’s tweet suggests, we’re doing it with artists working in new media and emerging technologies.

To be clear, I’m not saying that failing to recognize the pioneers of art and technology is as morally wrong as discriminating against people for the characteristics inherent in their humanity. But I argue that ignorance or strictly superficial engagement with technology creates similar blind spots in art, as the same attitudes towards gender, sexuality, ethnicity and geography have done in the past. .

By and large, we in the mainstream art world don’t do the research. We do not seriously engage with unknown communities. We don’t question the story told to us by people with the loudest and most expensive megaphones.

But it is still early. The dominant narrative about art and machine learning is not yet settled. We can still correct the distortions.

However, if we don’t make this effort, it will be irrefutable proof of how little we have really learned, not about machines, but about others and ourselves. And perhaps worst of all, it will inspire us to repeat the same historic mistake all over again elsewhere. In other words, if we don’t already.

[artnet News]

It’s all for this week. “Until next time, remember: there may be a lot of work to be done on artificial intelligence, but the same can be said for natural intelligence.

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