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Modeling and Correcting Bias in Sequential Evaluation. (arXiv:2205.01607v1 [stat.ML])
May 4, 2022, 1:11 a.m. | Jingyan Wang, Ashwin Pananjady
cs.LG updates on arXiv.org arxiv.org
We consider the problem of sequential evaluation, in which an evaluator
observes candidates in a sequence and assigns scores to these candidates in an
online, irrevocable fashion. Motivated by the psychology literature that has
studied sequential bias in such settings -- namely, dependencies between the
evaluation outcome and the order in which the candidates appear -- we propose a
natural model for the evaluator's rating process that captures the lack of
calibration inherent to such a task. We conduct crowdsourcing …
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