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- W4229013481 abstract "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 experiments to demonstrate various facets of our model. We then proceed to study how to correct sequential bias under our model by posing this as a statistical inference problem. We propose a near-linear time, online algorithm for this task and prove guarantees in terms of two canonical ranking metrics. We also prove that our algorithm is information theoretically optimal, by establishing matching lower bounds in both metrics. Finally, we show that our algorithm outperforms the de facto method of using the rankings induced by the reported scores." @default.
- W4229013481 created "2022-05-08" @default.
- W4229013481 creator A5031857164 @default.
- W4229013481 creator A5085869933 @default.
- W4229013481 date "2022-05-03" @default.
- W4229013481 modified "2023-09-28" @default.
- W4229013481 title "Modeling and Correcting Bias in Sequential Evaluation" @default.
- W4229013481 doi "https://doi.org/10.48550/arxiv.2205.01607" @default.
- W4229013481 hasPublicationYear "2022" @default.
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