Abstract:
Data science methods are increasingly being applied to large-scale educational data, but there has been less attention on the possibility of algorithmic bias where algorithms can potentially make predictions that result in decisions that are unfair to certain population subgroups. In this presentation, we present several metrics used for algorithmic bias, discuss how proportions of sensitive groups can impact the presence of algorithmic bias, and provide some preliminary recommendations for researchers.
Bio(s):
Presented by:
Ashani Jayasekera, PhD Candidate, Quantitative Methodology: Measurement and Statistics, University of Maryland, College Park
Ashani Jayasekera is a doctoral candidate in the Quantitative Methodology: Measurement and Statistics program at the University of Maryland, College Park. She earned a MS in Measurement, Statistics & Evaluation from the University of Maryland, College Park and a BS in Mathematics from the University of Maryland, Baltimore County. Her research interests include machine learning, natural language processing, the analysis of complex data structures, as well as the intersection of data science and QuantCrit. Recent research projects include work on the efficacy of supervised machine learning in causal inference, the utilization of propensity scores to provide measures of school quality, and the impacts of missing data on social network models.
SoDa Seed Grant Award Recipient:
Tracy Sweet, Associate Professor, Quantitative Methodology: Measurement and Statistics, Department of Human Development and Quantitative Methodology
Tracy Sweet is an Associate Professor in the Quantitative Methodology: Measurement & Statistics (QMMS) program in the Department of Human Development and Quantitative Methodology. She completed her Ph.D. in Statistics at Carnegie Mellon University and a M.A. in Mathematics at Morgan State University. Her research focuses on methods for social network analysis and machine learning. She serves as the Associate Director of Research for UMCP for the Maryland Longitudinal Data System Center and is currently overseeing projects applying data science and statistical methods to large-scale educational data. Finally, Dr. Sweet is committed to improving equity in the fields of statistics and quantitative methodology.
Moderated by: Frauke Kreuter, a Professor in the Joint Program in Survey Methodology and Director of the Social Data Science Center at the University of Maryland. She also serves as Chair of Statistics and Data Science in Social Sciences and Humanities at Ludwig-Maximilian University of Munich.
Registration details found here.
