AI RESEARCH
An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration
arXiv CS.LG
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ArXi:2605.18648v1 Announce Type: new Central to human-aligned AI is understanding the benefits of human-elicited labels over synthetic alternatives. While human soft-labels improve calibration by capturing uncertainty, prior studies conflate these benefits with the implicit correction of mislabeled data (mode shifts), obscuring true effects of soft-labels. We present a controlled audit of soft-label learning across MNIST and a synthetic variant, re-annotating subsets to extract human uncertainty.