Commenters identify confidence score interpretation as a core problem
4 Yesterday 1:10 PM · 1d ago · 1 post · 2 comments · 2 sources · development 4 of 7
Multiple voices highlight how AI confidence scores mislead business stakeholders into false assumptions about reliability, linking the issue to broader misuse of statistics in ML deployment decisions.
“An algorithm doesn't have "confidence" in the way that a person has confidence, but as soon you put something with that name in front of a business person they assume the number is always a meaningful "letter grade curve" or "universal percentage".”
WorldMakerihatethefuture.com author Article authoroli5679 AI systems engineer (ops automation)adamddev1 Commenterbenjaminsky2 Developer with validation experience
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What people said 2 voices · verbatim
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"Confidence scores" have always implied an anthopocentric meaning that doesn't exist. An algorithm doesn't have "confidence" in the way that a person has confidence, but as soon you put something with that name in front of a business person they assume the number is always a meaningful "letter grade curve" or "universal percentage". I still…
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I am big on reproducibility (nix aficionado) and determinism (flagging test failures are a red-alert, all-hands-on-deck situation in my world) and correctness.I am also big on testing (the correct things). And nine-nines (big on Elixir).And... I'm also big on agent-assisted dev. Which requires pretty much every check in the book to stay productive…
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