SoDa Symposium | Seed Grant Series: Data Preprocessing Strategies to Enhance Fairness in Machine Learning
A presentation with Q&A
presented on
Tuesday, October 21, 2025
12 pm – 1 pm (Online)
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.
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?
Ashani Jayasekera
Ph.D. Candidate, Quantitative Methodology: Measurement and Statistics (QMMS)
University of Maryland
SoDa Seed Grant Award Recipient:
Tracy Sweet, Ph. D. (PI)
Associate Professor
Quantitative Methodology: Measurement and Statistics Program
Department of Human Development and Quantitative Methodology
SoDa Seed Grants: The projects under this initiative may address any societal challenge that affects a large number of people, including but not limited to health, public safety, justice, race, gender, education, employment, transit, and political representation. The goal of these seed grants is to encourage faculty to develop collaborative projects that stimulate the advancement of new ideas that can build the university’s expertise toward a national reputation in the broad area of social data science. The projects blend the development or use of innovative data science methods or new measurements, the advancement of scholarship within or across disciplines, and progress in addressing a societal challenge.
