AI RESEARCH
Principled Evaluation with Human Labels: One Rater at a Time and Rater Equivalence
arXiv CS.LG
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ArXi:2106.01254v3 Announce Type: replace In many classification tasks, there is no definitive ground truth, only human judgments that may disagree. We address two challenges that arise in such settings: (1) how to use human raters to score classifiers, and (2) how to use them for comparison benchmarks. For the first, the common practice is to score classifiers against the majority vote of an evaluation panel of several human raters. We argue that this is not justified when either of two properties fails: objectivity or equanimity.