Beyond the Hype: AI in Qualitative and Historical Research

Registration is now closed. We encourage you to email csus@utoronto.ca to join the waitlist, as additional spots may open closer to the event date.

 

This event brings together leading researchers from across the social sciences and humanities to explore the practical realities of integrating AI into qualitative and historical research. Experts from a variety of fields, including sociology, history, geography, and political science, will provide candid, nuts-and-bolts accounts of how they use AI tools in their work, from leveraging large language models to process archival texts and spatial data, to using generative AI for coding and classification of qualitative data, to grappling with document digitization and data linkage. They will address the practical challenges that arise at every stage of the research process, including crafting effective prompts, interpreting and validating AI-generated output, detecting bias, and knowing when AI helps and when it gets in the way.

 

This event is co-organized by the Centre for The Study of the United States (Munk School of Global Affairs & Public Policy) and Data Science Institute, University of Toronto.

 

Speakers:

S. Wright Kennedy is an Assistant Professor in the History Department at the University of South Carolina. He specializes in public-facing spatial history projects, and he uses geographic information systems (GIS) and spatial analysis to study past and present health, environmental, and socioeconomic issues. Professor Kennedy has investigated a wide range of spatial history topics with GIS, including epidemics, streetcar corruption, hurricane recovery, residential segregation, and environmental injustices. Previously, he led the Mapping Historical New York (mappinghny.com) project for four years as a postdoctoral scholar at Columbia University and served as project manager for three years on the imagineRio project (imaginerio.org) at Rice University. He has a PhD in History, an MA in Geography, and is a certified GIS Professional (GISP). Professor Kennedy’s teaching interests include spatial history methods, public history, and the history of public health. He is working on his first monograph, tentatively titled Separate but Dead, which examines the rise of residential segregation in New Orleans at the end of the 19th Century and the unequal burdens of disease that segregation created.

 

Sergio Petralia is an assistant professor at Utrecht University. He was previously a postdoctoral researcher at the London School of Economics and Political Science and was a visitor at the Harvard Growth Lab as a Visiting Fellow. He holds a Bachelors in Economics from the University of Buenos Aires, a Masters in Economics from the University of San Andres in Buenos Aires, and Msc in Economics from Pennsylvania State University in the U.S. He finished his Ph.D. at Utrecht University in 2017. Sergio is currently working on issues related to technological change and innovation. His most recent research projects study the emergence and spatial concentration of new technologies using historical data on patent activity, the identification of the challenges and opportunities for technological development in developing economies, and the impact of disruptive technological change on income and wages.

 

Ian Milligan (he/him) is a Professor of History at the University of Waterloo and a Fellow of the Royal Historical Society. He has authored or co-authored six books, most recently Averting the Digital Dark Age (Johns Hopkins, 2024). As Associate Vice-President, Research Oversight & Integrity, he provides campus-wide leadership across research ethics, compliance, safeguarding, and research data management, co-leading the university’s RDM strategy through to implementation. As PI of the Archives Unleashed project (2017–2023), he worked with and helped lead an interdisciplinary, multi-institution research team whose work is now offered as a service by the Internet Archive.

 

Alina Arseniev-Koehler is a computational and cultural sociologist with substantive interests in language, health, and social categories. Alina strives to clarify core concepts and debates about cultural meaning in sociology. For example, how do individuals learn and deploy stereotypes? Empirically, Alina focuses on cases where meaning is linked to inequality and health, such as the moral meanings attached to body weight, the stigmatizing meanings of disease, and gender stereotypes. To investigate these topics, Alina uses computational methods and machine learning, especially computational text analysis.

 

Daniel Karell’s research uses computational and quantitative methods to examine the intersection of social movements, culture, and technology. For example, some projects investigate how people’s interactions with AI can influence their understanding of the social world, including their perceptions of history and behavior towards people in different social groups. Another project analyzes the social and cultural dynamics of backlash, with a focus on the Blue Lives Matter movement during 2020. Daniel’s research has appeared in several academic journals, including American Sociological Review, Sociological Methods & Research, Sociological Methodology, and PNAS Nexus. His work has won awards from the American Sociological Association’s section on Collective Behavior and Social Movements and the Journal of Peace Research. In recent years, Daniel has been a Weatherhead Scholar at Harvard University and a Fung Global Fellow at Princeton University. At Yale, Daniel is a faculty affiliate of the Institute for Foundations of Data Science and the Institution for Social and Policy Studies. He is also a co-organizer of the Computational Social Science Workshop.(Link is external) Daniel teaches courses on integrating AI into social science research methods, computational approaches to studying culture, and the sociology of backlash.

 

Ethan Fosse is an Associate Professor of Sociology, Associate Director of the Data Sciences Institute, Faculty Affiliate with the Centre for Analytics and Artificial Intelligence Engineering, and Co-Director of the Consortium on Advanced Social Analytics at the University of Toronto. Before joining the University of Toronto, he received his Ph.D. from Harvard University and worked as a Postdoctoral Research Associate at Princeton University in the Department of Sociology and the Department of Politics, where he developed and led a series of open-source data science and computer programming workshops. At the University of Toronto, Professor Fosse teaches courses on artificial intelligence, social change, and data science.

Professor Fosse's research focuses on applying innovative computational and quantitative methods to understand social change, inequality, and social connectedness. He is primarily engaged in three interrelated projects: first, creating a new set of techniques for cohort analysis, with wide application in sociology and related fields; second, applying and extending these methods to various substantive areas, from verbal ability to political party identification to social mobility; finally, developing and applying novel data-driven artificial intelligence (AI) methods, such as graph neural networks and transformer-based models, to answer pressing social science questions. Professor Fosse's research has been published in numerous volumes and journals, with recent work appearing in Demography, Sociological Science, Sociological Methodology, Sociological Methods and Research, and the Annual Review of Sociology.