Data Science Education Symposium

Date: Monday, April 13 and Tuesday, April 14, 2026

Location: University of California, Los Angeles

This symposium brings together researchers and educators in K-12 and tertiary data science, statistics, and AI education to foster discussion on ways in which data science education can promote student success.

Please register by Monday, March 16, 2026

Title: From Algorithmic Biases to Critical AI Literacy

Abstract: As AI tools accelerate into classrooms and workplaces, many of students are expressing anxiety about what these technologies will mean for their futures. While teaching AI literacy in K-12 contexts, not only did we observe “bias” in large language models, we observed an amplification of entrenched power imbalances where racialized, gendered, and sexualized hierarchies were reproduced and intensified through seemingly innocuous writing tasks. These patterns prompted a collaborative, student-centered research agenda that culminated in an AI experiment systematically probing how leading models from OpenAI, Google, Meta, and Anthropic generate harmful and stereotypical narratives of minoritized identities. Our findings demonstrate that these outputs disproportionately target minoritized students, but also harm non-minoritized students by normalizing inequitable ways of interacting, imagining, and collaborating, ultimately undermining the development of real-world learning and work environments.

What does it mean for data science education when teaching AI risks reinscribing the very inequities we are working to dismantle? How might we combat these power imbalances by drawing on the legacies of past and current critical algorithmic and education scholars who teach us to interrogate, refuse, and reimagine oppressive technologies? As we consider how to cultivate future generations of AI-literate citizens within the goals of Data X Presents: Symposium on Shaping the Future of Data Science Education—Building Bridges from K-12 to Beyond, this talk offers both caution and possibility for building more empowering sociotechnical futures.

Thema (Tay-mah) Monroe-White is an Associate Professor of Artificial Intelligence and Innovation Policy in the Schar School of Policy and Government and the Department of Computer Science at George Mason University. See full bio on the speakers page.

Evan Shieh works as an AI researcher and educator focusing on culturally relevant data science and AI justice. See full bio on the speakers page.

UCLA DataX

Learn more about DataX here.