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Over the two centuries since its invention, the device has become a prevalent metaphor for technological manipulation. It represents the human lurking behind the facade of seemingly advanced technology as well as the ability of such devices to deceive us about the way they work.
Surely there is more to my identity as a consumer of culture?
Filterworld, the title of this book, is my word for the vast, interlocking, and yet diffuse network of algorithms that influence our lives today, which has had a particularly dramatic impact on culture and the ways it is distributed and consumed.
Filterworld culture is ultimately homogenous, marked by a pervasive sense of sameness even when its artifacts aren’t literally the same. It perpetuates itself to the point of boredom.
an international “harmonization of tastes.”
of such algorithmic gatekeeping is the pervasive flattening that has been happening across culture. By flatness I mean homogenization but also a reduction into simplicity: the least ambiguous, least disruptive, and perhaps least meaningful pieces of culture are promoted the most. Flatness is the lowest common denominator, an averageness that has never been the marker of humanity’s proudest cultural creations.
Restoration and Reduction into Latin. Al-jabr became “algeber,” and al-Khwarizmi became “Algoritmi.” At that time, “algorismus” referred generally to any kind of mathematical procedure using Hindu-Arabic numerals, and those who practiced such an art were called algorists.
The signal is fed through a data transformer that puts it into usable packages, set to be processed by different kinds of algorithms. Engagement data might need to be separated from ratings data,
or data about the subject matter of the content itself.
the machine adapts to users and users adapt to the machine.
Their email filtering system, called Tapestry, used two kinds of algorithms in tandem: “content-based filtering” and “collaborative filtering.” The former, which was already used in several email systems, evaluated the text of emails—say, if you wanted to prioritize everything with the
word algorithm. But the latter, more innovative technique was based on the actions of other users. Who opened a particular email and how they responded to it would be factored into how much the system prioritized the email.
People collaborate to help one another perform filtering by recording their reactions to documents they read. Such reactions may be that a document was particularly interesting (or particularly uninteresting). These reactions, more generally called annotations, can be accessed by others’ filters.
In 1995, a paper from Upendra Shardanand and Pattie Maes at the MIT Media Lab described “social information filtering,” “a technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that user and those of other users.”
“We need technology to help us wade through all the information to find the items we really want and need, and to rid us of the things we do not want to be bothered with.”
New tools like ChatGPT seem to be able to understand and generate meaningful language, but really, they only repeat patterns inherent in the preexisting data they are
“Items are recommended to a user based upon values assigned by other
people with similar taste,”
according to the paper. The similarity of one user’s taste to another was calculated usin...
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Recommendations were the goal: recommending a piece of information, a song, an image, or a social media update. Algorithmic feeds are sometimes more formally and literally labeled “recommender systems,” for the simple act of choosing a piece of content.
PageRank worked by measuring how many times a website was linked to by other sites, similar to the way academic papers cite key pieces of past research. The more links, the more important a page was likely to be. The metric
of citation “corresponds well with people’s subjective idea of importance,” Brin and Page wrote in a 1998 paper, “The Anatomy of a Large-Scale Hypertextual Web Search Engine.” PageRank mingled a form of collaborative filtering with content filtering.
Decades later, PageRank has become almost tyrannical, a system that dominates how and when websites are seen. It’s vital for a business or resource to make it to that first page of Google Search results by adapting to the PageRank
algorithm.
The ads a user sees were just as informed by the algorithm as the search results were. And advertising, built on the search algorithm, turned Google into a behemoth.
