Confidence Calibration under Ambiguous Ground Truth
📰 ArXiv cs.AI
Confidence calibration fails when annotators disagree on ground truth labels, leading to miscalibration despite appearing well-calibrated under conventional evaluation
Action Steps
- Recognize the limitation of traditional confidence calibration methods
- Understand the impact of annotator disagreement on model calibration
- Develop strategies to address miscalibration, such as using annotator distributions or alternative evaluation metrics
Who Needs to Know This
Machine learning engineers and researchers benefit from understanding this concept to improve model calibration and reliability, especially when working with ambiguous or uncertain data
Key Insight
💡 Confidence calibration assumes unique ground-truth labels, but this assumption fails when annotators genuinely disagree
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🚨 Confidence calibration can fail when annotators disagree! 🤔
Key Takeaways
Confidence calibration fails when annotators disagree on ground truth labels, leading to miscalibration despite appearing well-calibrated under conventional evaluation
Full Article
Title: Confidence Calibration under Ambiguous Ground Truth
Abstract:
arXiv:2603.22879v1 Announce Type: cross Abstract: 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. We show that this failure is structural: under simplifying assump
Abstract:
arXiv:2603.22879v1 Announce Type: cross Abstract: 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. We show that this failure is structural: under simplifying assump
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