Shaping & Diffusing AI Norms

​This session will examine how AI safety frameworks diffuse across higher education, K-12 schools, and government bodies that affect learning and research. The discussion will draw on perspectives from policy studies, sociology of institutions, and science and technology studies. We will explore:

• ​How policy ideas move across institutions, and how they change as they travel.

• ​How norms are constructed, stabilized, and diffused, including which actors have agenda-setting power

• ​How equity and representation are embedded in frameworks, including which perspectives or educational contexts are systematically underrepresented.

​The reading group brings together UBC students from all academic levels with faculty members to engage collectively with scholarship in AI policy. No prior technical background in AI or policy is required.

​Dr. Nelson uses computational methods—principally text analysis, natural language processing, machine learning, and network analysis—to study social movements, culture, gender, and organizations and institutions. Her research examines processes around the formation of collective identities, social movement strategies in feminist and environmental movements, the role of place in shaping activism, intersectionality in women’s movements and 19th-century U.S. South experiences, gender inequality in startups and STEM fields, and how academic ideas are translated into practice through programs like NSF’s ADVANCE. Her current projects include using AI and machine learning to understand intersectionality in women’s movements, media coverage of social movements over time, historical memory, and gender inequality across different sectors.