SoDa Symposium|Seed Grant Series: Data Processing Strategies to Enhance Fairness in Machine Learning

Описание: SoDa Symposium: Data Preprocessing Strategies to Enhance Fairness in Machine Learning
A Presentation with Q&A
Tuesday, October 21, 2025
12:00 pm - 1:00 pm EST

Presenters:
Presenter 1:
Ashani Jayasekera, PhD candidate, Quantitative Methodology: Measurement and Statistics 
Bio:
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. 
 
Presenter 2:
SoDa Seed Grant Award Recipient: Tracy Sweet, PhD, Associate Professor, Department of Human Development and Quantitative Methodology
Bio:
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. 

Moderator:
Frauke Kreuter
Professor
Joint Program in Survey Methodology
Director, Social Data Science Center (SoDa)
University of Maryland
Chair of Statistics and Data Science in Social Sciences and Humanities
Ludwig-Maximilian, University of Munich

Tracy Sweet SoDa Seed Grant Proposal Abstract:
The following proposal focuses on statistical methodological research for improving racial equity in the social sciences. Much of what we know about achievement differences between racial groups comes from large-scale statistical analyses. What if the data science methods we are using are contributing to this inequity? I propose two lines of inquiry to both better understand deficits in our current approaches that will ultimately improve data science methods for racial equity research. My proposed work focuses on race and educational outcomes, but the methodology and knowledge created by this research will be applicable to all minoritized groups as well as a wide range of disciplines in the social sciences.
To determine if current methods are working for racial equity, I will focus on the following goals:
Aim 1: How can we determine if data science methods are equitable for all racial groups?
Aim 2: To what extent are constructs (e.g. intelligence, motivation) accurately measured among different races?

The SoDa Center at UMD

SoDa Symposia highlight the diverse challenges and opportunities in the emerging area of Social Data Science.   Combining insights of SoDa researchers and partners from UMD and around the world, these regular virtual events showcase research and expert commentary about advances and open problems in the use of surveys, administrative, and trace data to understand and shape the social world we live in. Ranging from technical challenges of gathering high-quality data, ethical management of social data at scale, or examples of the power of social data science in education, business, government, or civic life, SoDa Symposia provide an opportunity for a broad audience of researchers, students, and practitioners to learn more about the potential of social data science to change the world.