Algorithmic culture. “Culture now has two audiences: people and machines”

A conversation with Ted Striphas


(#TheMakingOfaNewBook)
Ted Striphas, author of
The Late Age of Print: Everyday Book Culture from Consumerism to Control, teaches at Indiana University in the United States. He is currently at work on his next book, Algorithmic Culture.

How are technology and culture shaping each other?

This is a difficult question, but only because we cannot presume to know in advance what “technology” and “culture” mean. For my part, I believe it’s always better to think of both as moving targets.

Technology and culture can “shape” or “influence” each another if and only if one proceeds from the assumption that they are separable, conceptually or semantically. For most of the past two centuries this has effectively been the case, but it is has not always been so. Until about 1800, the word “culture” in English referred to husbandry—that is, to techniques for tending crops and domesticated animals, including selective breeding. Sometimes it was used interchangeably with the world “coulter,” which is a part of a plough. Technology and culture used to be very closely aligned, so much so that it was difficult to imagine the one apart from the other.

That changed with the coming of modernity, when culture started to take on a more distinctly humane connotation. It was only then that one could begin to imagine technology corrupting culture, as the German philosophers Max Horkheimer and Theodor Adorno famously did back in 1944, in their critique of the “culture industry,” and as have the many who have followed in their wake.

Today, however, we are experiencing a shift with regard to the meaning of technology and culture. Though not a return exactly to the pre-modern understanding, it seems clear that both words are fusing back together in interesting ways. You can see this through the emergence of any number of phenomena, but consider two: Google and the digital humanities.

Google uses electricity, silicon, and plastic, all working in conjunction with an army of human engineers, to rank the importance of people, places, objects, and ideas. Though the means and ends are different, this is akin to what, back in 1869, the English literary critic Matthew Arnold said was the purpose of culture: to determine “the best which has been thought and said.”

Likewise, the digital humanities uses computational tools to tell people things about cultural goods that they cannot fully adduce themselves—a previously unrecognized literary genre subtending across several thousand novels, for instance, or the identity of an anonymous author, determined by parsing the stylistic idiosyncrasies of tens-of-thousands of authors.

What does it say about “human” culture, then, when key aspects of it may be intelligible only to machines?

How will you define the “Culture of Algorithms”?

My preferred phrase is “algorithmic culture,” which I use in the first instance to refer to the the ways in which computers, running complex mathematical formulae, engage in what’s often considered to be the traditional work of culture: the sorting, classifying, and hierarchizing of people, places, objects, and ideas. The Google example from above illustrates the point, although it’s also the case elsewhere on the internet. Facebook engages in much the same work in determining which of your friends, and which of their posts, will appear prominently in your news feed. The same goes for shopping sites and video or music streaming services, when they offer you products based on the ones you (or someone purportedly like you) have already consumed.

What’s important to note, though, is the way in which algorithmic culture then feeds back to produce new habits of thought, conduct, and expression that likely wouldn’t exist in its absence—a culture of algorithms, as it were. The worry here, pointed out by Eli Pariser and others, is that this culture tends to reinforce more than it challenges one’s existing preferences or ways of doing things. This is what is often called “personalization,” though Pariser calls it a “you loop” instead. By the same token, it is possible for algorithmic systems to introduce you to cultural goods that you might not have encountered otherwise. Today, culture may only be as good as its algorithms.

Robots, artificial intelligence, algorithms: what we have to expect in the near future?

I do not know much about robots, although I gather huge strides are being made there, especially in the move away from strong- to weak- or distributed-AI. Having said that, I would expect algorithms and large-scale computation to continue to make inroads deeper and deeper into the realm of daily affairs. One can already see this with Google’s self-driving cars, which, regardless of how one feels about Google, are an extraordinary achievement in engineering.

The issue may come down to how comfortable people are with these systems drilling down into our daily lives, and even becoming extensions of our bodies. I’m thinking here of Google Glass, for instance, which connects your head—or really your face—to the power of Google. Here’s another extraordinary, computationally-intensive device that’s been banned in more than a few places in the United States, owing to privacy concerns. And yet, it’s less capable than most smart phones. Why all the fuss, then?

The face is one of the most culturally-charged aspects of the human body, so it’s little wonder why turning it into a computer would raise difficult questions. The future will involve not only sophisticated engineering, then, but also efforts to redefine how people make sense our bodies and the specific parts to which we decide to attach computational tools. That’s largely a social question, however much it may overlap with the technical.

What do you think are the forces and trends that are driving the change?

Obviously, well-capitalized players such as Google, Apple, Facebook, Amazon, Netflix, and others are helping to drive these changes, though they are not singularly responsible for them. The deeper I delve into my research on the history of algorithmic culture, the more I come to believe that the discovery of “information” and the subsequent growth of informatics were decisive turning points.

The concept of information has allowed any number of seemingly disparate phenomena—from genetic material to the temperature inside one’s home, the content of a novel, the sound transmitted over a phone line, and more—to be imagined as comparable in some way…and also then, analyzable. My phone records the number of steps I take during the day, not to mention my whereabouts, and who knows what else? Aspects of our lives are now being fitted with sensors that produce a whole range of mundane activities as information-bearing. Perhaps they were expressive in this manner all along, but only now is the technology being developed to gather and parse the information in some sense systematically.

For me, then, the notion that everything is—or is potentially—informatic is a key driver of the changes we’re seeing. This notion has only been around since about the 1940s.

How do you envision the future of the Cultural Industry?

I see at least key two trends emerging. Both involve the use of algorithms in cultural production.

The first example that comes to mind is that of Narrative Science. The company scrapes all sorts of information from the worlds of sports and business; its algorithms then parse it, narrativizing it into news stories. I would expect to see more such systems coming online over the next several years and decades. All sorts of “routine” storytelling, if indeed there is such a thing, may well be rendered by machines in the not-to-distant future.

Second, algorithms are likely to have a hand in deciding which aspects of culture get to be green-lighted for production. Some movie scripts are now being subjected to algorithmic scrutiny in search of a sufficient number of elements characteristic of blockbusters. Similarly, Netflix evidently analyzes customer data before approving any of its original programming, making sure that what it knows about its customers’ viewing tastes and habits is commensurate with a new show’s cast, genre, structure, and so forth.

Culture now has two audiences, in other words: people and machines. Both will have a significant hand in shaping the material that finds its way into the public realm.

Twitter: @gg

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