Verified75% confidenceFactExact time
Many neural networks exhibit an overconfidence calibration bias, where output confidence scores remain close to 1 even when the error rate is high.
3
Sources
75%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
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Related Claims
UnverifiedCalibration does not equal high accuracy; a system that reports 95% confidence on everything may still be overconfident or blindly optimistic even if its accuracy reaches 95%.74% similarUnverifiedMany models still exhibit high confidence in incorrect answers, presenting a confidence calibration problem.72% similarUnverifiedAverage accuracy masks distributional skew; a judge that gets all easy cases right and is overconfident on hard cases can post a high average score while failing at critical points.72% similarUnverifiedGeneral-purpose large models commonly suffer from overconfidence issues in confidence calibration, tending to output confidence levels close to 100%.71% similarUnverifiedIn the task of predicting final exam scores from midterm scores, a basic neural network using only students' own scores has high error.71% similar
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