AI RESEARCH
Confidence Calibration under Ambiguous Ground Truth
arXiv CS.AI
•
ArXi:2603.22879v1 Announce Type: cross Confidence calibration assumes a unique ground-truth label per input, yet this assumption fails wherever annotators genuinely disagree. Post-hoc calibrators fitted on majority-voted labels, the standard single-label targets used in practice, can appear well-calibrated under conventional evaluation yet remain substantially miscalibrated against the underlying annotator distribution.