Center for Signals and Information Processing (CSIP) Seminar
Date: Friday, February 9, 20234
Time: 3:00 p.m.
Location: CSIP Library 5126. The associated zoom link is: https://gatech.zoom.us/j/96047523378
Speaker: Jingyan Wang
Speakers' Title: Postdoctoral Fellow in Industrial and Systems Engineering at Georgia Institute of Technology
Seminar Title: Understanding and Improving High-Stakes Decision Making
Abstract: Quantization is the process of approximating a continuous signal through a set of discrete values. While physical quantities are precise, when digitized and inserted into a computer or a digital system, they are rounded to the nearest representative value. This process introduces quantization error, i.e., noise. This talk will introduce how to deal with digital systems where quantization is applied using statistical description of the process. Furthermore, fixed- and floating-point quantization will be also addressed. Finally, some examples will be shown where quantization play an important role.High-stakes evaluation problems – to estimate the quality of items or people – arise in many real-world applications such as admissions, grading, and hiring. My research focuses on understanding and improving these evaluation problems along the dimensions of accuracy, fairness, efficiency, and reliability. In this talk, I describe one line of my research on correcting human bias under different behavioral models. The first type of bias arises in a sequential setting such as sports competitions and court decisions, where an evaluator rates candidates in an online, irrevocable fashion. I propose a natural model for the evaluator's rating process that captures the lack of calibration inherent to this task, and conduct crowdsourcing experiments to support various facets of the model. I propose an efficient bias correction algorithm and show that it is information-theoretically optimal in terms of common ranking error metrics. I also briefly describe a second type of bias arising from people’s experiences that are irrelevant to the evaluation objective. For example, in teaching evaluation, students who receive higher grades are more positive towards their instructors. In such scenarios, I propose mild non-parametric assumptions to model the bias, and design an adaptive algorithm to correct student ratings.
Bio: Jingyan Wang is a postdoctoral fellow in Industrial and Systems Engineering at Georgia Tech. She received her Ph.D. from the School of Computer Science at Carnegie Mellon University, advised by Nihar Shah, and her B.S. in Electrical Engineering and Computer Sciences with a minor in Mathematics from the University of California, Berkeley. She uses tools from statistics and machine learning to understand and improve high-stakes decision-making systems such as those involving hiring, admissions, and peer review. Her interdisciplinary research has been published in statistics, machine learning, artificial intelligence, human computation, and economics and computation. She is the recipient of the Best Student Paper Award at AAMAS 2019, and was selected as a Rising Star in EECS and in Data Science.