A statistical framework decomposes human annotation outcomes into four interpretable variation sources and extends classical measurement-error models to handle both shared and individualized notions of truth.
” garbage in, garbage out” revisited: What do machine learn- ing application papers report about human-labeled training data?arXiv preprint arXiv:2107.02278
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From Ground Truth to Measurement: A Statistical Framework for Human Labeling
A statistical framework decomposes human annotation outcomes into four interpretable variation sources and extends classical measurement-error models to handle both shared and individualized notions of truth.