BRIDGING THE GAP: AI Literacy for the Next Gen of Pop Researchers
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Join us for two days exploring the ethics, tools, and practical applications of generative AI in research, through expert-led sessions, hands-on workshops, and collaborative discussion.
Register for Session 2, Session 3, or both. Please note that Session 1 is not available for virtual attendance. This registration is for virtual attendees only. For in-person attendance, please contact cand3 [at] mcgill.ca.
Itinerary
Session 1: Ethics of AI with Nicholas King
(Open only to BRIDGE team members)
September 10, 2026 | 13:30 - 16:45 (with a coffee break 15:00 - 15:15)
This training session will examine ethical dilemmas raised by the use of data in formulating, implementing, and evaluating decisions and policies. It particularly focuses on balancing the risks and benefits of using data for policy and addressing the unintended consequences of data-driven policymaking.
More about Nicholas King
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Nicholas KingAssociate Professor, Department of Ethics, Equity, and Policy, McGill University Nicholas B. King, PhD, is an associate professor at McGill University, with appointments in the Dept. of Equity, Ethics & Policy; Dept. of Epidemiology, Biostatistics and Occupational Health; and the Max Bell School for Public Policy. His primary research areas are public health policy, ethics, and epistemology. He studies the ways that 'black boxes' of all sorts - from seemingly objective measures of health and health inequalities, to complex algorithms - are shaped by human interests and hidden value judgments, which in turn shape individual decisions, collective behaviors, and public policies.
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Session 2: Panel – Curated Overview of Gen AI Tools & Disclosure Framework - Moderated by Leah Macfadyen
September 11, 2026 | 11:00 - 12:00
This panel discussion brings together population researchers to discuss how they use generative AI tools in their work. Panelists, including Amélie Quesnel-Vallée, Sofia Gil-Clavel, and Christopher Barrington-Leigh, will each discuss the tools they've chosen to use, why they selected tools over others, what they find them most useful for, and what considerations they have for intentional and thoughtful use. The session will close with a short overview of the current state of and best practices for AI use disclosure requirements.
More about the panelists
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Amélie Quesnel-ValléeProject Director of BRIDGE, Executive Director, Consortium on Analytics for Data-Driven Decision-Making (CAnD3) Amélie Quesnel-Vallée is a Professor at McGill University, where she holds the Canada Research Chair in Department of Equity, Ethics and Policy (DEEP), School of Population and Global Health (SPGH), Faculty of Medicine and Health Sciences. She is the 2019-2020 recipient of the Fulbright Canada Distinguished Chair in Quebec Studies, SUNY Plattsburgh. She is also the founding Director of the McGill Observatory on Health and Social Services Reforms. Her research examines the contribution of policies to social inequalities in health over the life course. It appeared in journals such as The Lancet, the International Journal of Epidemiology, and Social Science & Medicine and was recognized through several international professional associations’ awards, including from the American Sociological Association, the Population Association of America, and the American Public Health Association. Committed to furthering public understanding of science, she is frequently sought by the media such as National Public Radio, the New York Times, and Business Week. In 2014, she was elected President of the International Sociological Association Research Committee on the Sociology of Health in recognition of her leadership in the field. She has served on several scientific advisory councils, including of Statistics Canada, and currently serves on the Governing Council (Board of Directors) of the Canadian Institutes of Health Research.
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Sofia Beatriz Gil-ClavelResearcher, Vrije Universiteit Amsterdam Sofia Gil-Clavel earned her Ph.D. from the Max Planck Institute for Demographic Research (Germany) and the University of Groningen (Netherlands). Prior to her doctoral studies, she completed a degree in Actuarial Science at the National Autonomous University of Mexico (UNAM) and earned an M.Sc. in Computer Science from the Mathematical Research Center of Mexico (CIMAT). During her Ph.D., she participated in several international training programs, including the International Max Planck Research School for Population, Health and Data Science (IMPRS-PHDS) in Germany and the Consortium on Analytics for Data-Driven Decision-Making (CAnD3) in Canada. Her research leverages Big Data sources, such as SCOPUS, Facebook, and Twitter, to investigate society’s drivers of climate change adaptation, older adults’ use of communication technologies, and migrants’ cultural integration. She applies a combination of computer science techniques (including machine learning and metaheuristics) with statistical and actuarial methods (such as generalized linear models, survival analysis, and bootstrapping).
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Christopher Barrington-LeighAssociate Professor, McGill University Originally trained in upper atmospheric and space plasma physics at M.I.T., Stanford, and Berkeley, Chris subsequently received a PhD in Economics at UBC. Chris' interests are focused on empirical and quantitative assessments of welfare, and their implications for economic, social, and environmental policy, including the pursuit of overall economic growth and material consumption expansion. In particular, his research makes use of subjective well-being reports to address the relative importance of social and community-oriented aspects of life as compared with material consumption. He uses large international as well as national surveys, experiments, and economic theoretical modeling to understand individual and aggregate consumption benefits. Chris was a Global Scholar of the Canadian Institute for Advanced Research (CIFAR), affiliated with CIFAR's research programme on Social Interactions, Identity and Well-Being.
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Session 3: AI Prompt Workshop with Leah Macfadyen
September 11, 2026 | 13:00 - 16:15 (with a coffee break 14:30 - 14:45)
This hands-on workshop builds prompting skills through real policy brief tasks drawn from previous CAnD3 sessions. Participants will work in pairs across a series of guided exercises: an initial attempt at prompting, followed by critique and a mini-lecture on good prompting practice; a revised attempt informed by that guidance; a comparison of two different AI tools or models; and an exercise using AI personas to give feedback on prompts and outputs. The workshop closes with a mini-lecture on AI disclosure statements, current expectations, good practice, and useful frameworks, followed by a final exercise applying the AID Framework to generate a disclosure statement for the policy brief completed earlier. Each activity is followed by group critique and discussion.
More about Leah Macfadyen
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Leah MacfadyenCo-Director of BRIDGE, Associate Professor, Department of Language and Literacy Education, University of British Columbia Leah Macfadyen is an Associate Professor of Teaching in the Department of Language and Literacy Education and the Master of Educational Technology (MET) program (UBC Faculty of Education). A broad-ranging interdisciplinarian, she has graduate training that spans the experimental sciences as well as the humanities and social sciences. Dr. Macfadyen’s early research in online learning drew on her interest in culture and intercultural communication, and was shaped by the realities of increasingly diverse learner communities in digital environments. Over the past decade, the growth of data captured by learning technologies—and the emergence of the field of learning analytics—has spurred Dr. Macfadyen to bring analytic and scientific approaches to the study of virtual learning. Her research examines how evidence from learning environments can be interpreted responsibly and used to improve learning design, feedback practices, and instructional decision-making. She has published qualitative, quantitative, and mixed-methods research in peer-reviewed venues across education, learning analytics, and digital learning, and she often works at the intersection of research and practice, using real course contexts to explore what supports (and constrains) meaningful change. In recent years, Dr. Macfadyen’s work has expanded to include AI literacy and the responsible integration of generative AI in higher education and professional learning. She is particularly interested in how AI tools reshape the everyday practices of learners and educators—how people make judgments, verify information, navigate uncertainty, and take responsibility when AI systems are involved in learning and assessed work. Alongside this, she is committed to critical conversations about data and AI literacy, equity, and the ethical and social implications of data-driven and AI-mediated education.
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Location
This is an online event hosted on Microsoft Teams. To receive details to enter the event, please register.
