Twitter’s autocomplete algorithm actively amplifies and entrenches the Naomi confusion

Evidence

I felt blood rush to my face. In that moment, nine months into living on the rock at the end of the road, thirteen months of remote teaching and indoor masking, an eternity since seeing my closest friends, it suddenly hit me: the confusion was now so frequent that Twitter’s algorithm was prompting it, helpfully filling in the mistake for its users, to save them precious time. This is how machine learning works—the algorithm imitates, learning from patterns. So, if my name is repeatedly mixed up with Wolf’s, even in jokes, then my name would start being suggested instead of hers, leading to even more mix-ups. Which also meant that anything I did to correct the record—or state my own position on what had become her pet topics—would just train the algorithm to confuse us even more. — Naomi Klein, Doppelganger, 125

Categorization

• XREF: This is the core methodological engine of the book — the doubling device retroactively validated by the algorithm. Connects to the reader’s purpose of tracking how confusion becomes an analytic instrument.
• SEED: The feedback loop of algorithmic identity-confusion is essay-worthy: the machine that learns from mistaken identity then cements it, making correction counterproductive.