Jingyan Wang
Bio: Jingyan Wang is a Ronald J. and Carol T. Beerman President’s postdoctoral fellow in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. Her research interests lie in improving high-stake decision-making problems such as hiring and admissions, using tools from statistics and machine learning. She is the recipient of the Best Student Paper Award at AAMAS 2019. She received her Ph.D. from the School of Computer Science at Carnegie Mellon University in 2021, advised by Nihar Shah. She received her B.S. in Electrical Engineering and Computer Sciences with a minor in Mathematics from the University of California, Berkeley in 2015.
Talk Title: Modeling and Correcting Bias in Sequential Evaluation
Talk Abstract: We consider the problem of sequential evaluation in applications such as hiring and competitions, 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 significantly outperforms the de facto method of naively using the rankings induced by the reported scores.