This viral exposé offers a chilling glimpse into the human machinery powering Artificial Intelligence. The author recounts their time working as a “data annotator”—a job often disguised by tech giants as “entry-level AI training.” Tasked with resolving impossible logic puzzles for pennies, they realized their human inputs weren’t fixing the system; they were creating the biased, illogical benchmarks algorithms blindly follow.
The essay argues that the “garbage in, garbage out” problem isn’t just about bad code, but about underpaid, stressed humans making arbitrary decisions to hit quotas. When these subjective judgments become training data, algorithms bake in bias and inefficiency, ultimately making life worse for everyone else.
It is a sobering reminder that behind every “smart” algorithm is a hidden, exploited workforce ensuring the machine keeps running.
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