"Taken together, the videos sometimes tell a simple story in algorithmic silhouette, with one bikini video after another, a cascade of talking sports heads, an unbroken flow of clothes to buy, or influencers talking over the News with a particular political orientation. Just as often, though, a stolen screen glance reveals a dismal anti-story, in which an AI model has guessed, in a whole bunch of different directions, what the user is "most likely to be interested in or engage with.""
"To you, the screen peeper, the spectacle is profoundly uncomfortable, not just because you're eavesdropping the output of an intimate machine-learning profile gleaned from private and often passive "signals" provided by someone you don't know, but because you know, deep down, that your chained-together clips - and the chains of people you know, love, and respect - would be exactly as alien, and alienating, to anyone outside of your narrow algorithmic cone."
Short-video platforms deliver endless vertical videos whose aggregate often forms a coherent algorithmic silhouette or a fragmented anti-story. Recommendation models collect content inventory, leverage many private and passive signals, make predictions about likely engagement, and rank clips by score. The resulting feeds can feel intuitive to the intended viewer while appearing incoherent or alien to an outside observer. Glimpsing another person’s feed exposes intimate machine-learning inferences and the narrowness of algorithmic profiles, revealing how users unknowingly participate in continuous A/B testing that pushes platforms to optimize for engagement over broader context or meaning.
Read at Intelligencer
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