Date: April 9, 2024
Time: 12:00pm – 1:00pm
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. In this presentation, we present several metrics used for algorithmic bias, discuss how proportions of racial groups impact the presence of algorithmic bias, and provide recommendations for researchers. Using simulated data and Maryland educational administrative data from the Maryland Longitudinal Data System Center, we provide a real-world illustrations of more and less equitable machine learning practices.
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.
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.