Applied AI / Teaching about AI across the curriculum
In 2026, we are focusing on how different disciplines teach AI technologies. Our experts from the arts, sciences, and humanities will give us valuable insights into how AI is changing their subjects, and how young people can be supported to develop the skills they need in the future. To learn more, watch the seminar recordings, read our summary blogs, and download speakers' slides.
Advancing AI for society via race-conscious algorithmic approaches (10 February 2026)
Speaker: Thema Monroe-White (George Mason University)
The increasing dependence on algorithmic systems across societal domains has underscored the need to address structural inequities encoded in scientific, and artificial intelligence (AI) systems. Thema Monroe-White presented her research on intersectional race and gender biases in large language models (LLMs) and scientific discourse with implications for educators and education research. She discussed why and how emancipatory data practices, empowering algorithmic design principles, and responsible innovation can help to mitigate systemic biases in scientific- and AI-driven decision-making. By utilising critical quantitative approaches, her research not only advances scientific discovery; it prioritises the needs and well-being of marginalised people. Her interdisciplinary approach highlights the value of centering lived experiences and personal identities in computational and quantitative methodologies to ensure the benefits of science and technology are equitably distributed.
Thema Monroe-White is an Associate Professor of Artificial Intelligence, and Innovation Policy at the Schar School of Policy and Government and the Department of Computer Science (joint) at George Mason University. Her interests include bias mitigation in artificial intelligence (AI), critical quantitative and computational methods, and racial equity in social and economic systems. She is particularly concerned with understanding the pathways to empowerment for minoritised groups via AI education and emancipatory data science practices. She serves as a senior advisor for multiple nonprofit, community, and philanthropic agencies on equitable pathways in data science education, race and gender equity in the AI workforce, and fostering diversity in STEM pathways.