Portrait de Jean-François Godbout

Jean-François Godbout

Membre académique associé
Professeur titulaire, Université de Montréal
Sujets de recherche
Désinformation
Modèles génératifs
Sécurité de l'IA

Biographie

Jean-François Godbout est professeur au département de science politique de l'Université de Montréal et membre académique associé à Mila - Institut québécois d'intelligence artificielle. Ses recherches portent principalement sur les sciences sociales computationnelles, la sécurité de l'IA et l'impact de l'IA générative sur la société. Il est actuellement directeur du programme de premier cycle en analyse des données en sciences humaines à l'Université de Montréal et chercheur à IVADO.

Étudiants actuels

Postdoctorat - UdeM
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Maîtrise recherche - UdeM
Co-superviseur⋅e :

Publications

From Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions
Ruben Weijers
Denton Wu
Hannah Betts
Tamara Jacod
Yuxiang Guan
Vidya Sujaya
Kushal Dev
William Delooze
Ying Wu
Kellin Pelrine
Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accur… (voir plus)acy and helping students become independent critical thinkers. In this study, we designed a helpful yet fallible AI "Peer" to help students correct fundamental physics misconceptions related to Newtonian mechanic concepts. In contrast to approaches that seek near-perfect accuracy to create an authoritative AI tutor or teacher, we directly inform students that this AI can answer up to 40\% of questions incorrectly. In a randomized controlled trial with 165 students, those who engaged in targeted dialogue with the AI Peer achieved post-test scores that were, on average, 10.5 percentage points higher—with over 20 percentage points higher normalized gain—than a control group that discussed physics history. Qualitative feedback indicated that 91% of the treatment group's AI interactions were rated as helpful. Furthermore, by comparing student performance on pre- and post-test questions about the same concept, along with experts' annotations of the AI interactions, we find initial evidence suggesting the improvement in performance does not depend on the correctness of the AI. With further research, the AI Peer paradigm described here could open new possibilities for how we learn, adapt to, and grow with AI.
Online Influence Campaigns: Strategies and Vulnerabilities
Andreea Musulan
Veronica Xia
Ethan Kosak-Hine
Tom Gibbs
Vidya Sujaya
Kellin Pelrine
U. Montr'eal
Ivado
McGill University
In order to combat the creation and spread of harmful content online, this paper defines and contextualizes the concept of inauthentic, soci… (voir plus)etal-scale manipulation by malicious actors. We review the literature on societally harmful content and how it proliferates to analyze the manipulation strategies used by such actors and the vulnerabilities they target. We also provide an overview of three case studies of extensive manipulation campaigns to emphasize the severity of the problem. We then address the role that Artificial Intelligence plays in the development and dissemination of harmful content, and how its evolution presents new threats to societal cohesion for countries across the globe. Our survey aims to increase our understanding of not just particular aspects of these threats, but also the strategies underlying their deployment, so we can effectively prepare for the evolving cybersecurity landscape.
A Simulation System Towards Solving Societal-Scale Manipulation
Maximilian Puelma Touzel
Sneheel Sarangi
Austin Welch
Gayatri Krishnakumar
Dan Zhao
Zachary Yang
Hao Yu
Ethan Kosak-Hine
Tom Gibbs
Andreea Musulan
Camille Thibault
Busra Tugce Gurbuz
Kellin Pelrine
The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-w… (voir plus)orld settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. We improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys. We demonstrate the simulator with a tailored example in which we track agents' political positions and show how partisan manipulation of agents can affect election results.
A Simulation System Towards Solving Societal-Scale Manipulation
Maximilian Puelma Touzel
Sneheel Sarangi
Austin Welch
Gayatri Krishnakumar
Dan Zhao
Zachary Yang
Hao Yu
Ethan Kosak-Hine
Tom Gibbs
Andreea Musulan
Camille Thibault
Busra Tugce Gurbuz
Kellin Pelrine
The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-w… (voir plus)orld settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. We improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys. We demonstrate the simulator with a tailored example in which we track agents' political positions and show how partisan manipulation of agents can affect election results.
Epistemic Integrity in Large Language Models
Bijean Ghafouri
Shahrad Mohammadzadeh
James Zhou
Pratheeksha Nair
Jacob-Junqi Tian
Mayank Goel
Kellin Pelrine
Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statem… (voir plus)ents with high confidence poses risks for users and society. In this paper, we confront the critical problem of epistemic miscalibration—where a model's linguistic assertiveness fails to reflect its true internal certainty. We introduce a new human-labeled dataset and a novel method for measuring the linguistic assertiveness of Large Language Models which cuts error rates by over 50% relative to previous benchmarks. Validated across multiple datasets, our method reveals a stark misalignment between how confidently models linguistically present information and their actual accuracy. Further human evaluations confirm the severity of this miscalibration. This evidence underscores the urgent risk of the overstated certainty Large Language Models hold which may mislead users on a massive scale. Our framework provides a crucial step forward in diagnosing and correcting this miscalibration, offering a path to safer and more trustworthy AI across domains.
Epistemic Integrity in Large Language Models
Bijean Ghafouri
Shahrad Mohammadzadeh
James Zhou
Pratheeksha Nair
Jacob-Junqi Tian
Mayank Goel
Kellin Pelrine
Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statem… (voir plus)ents with high confidence poses risks for users and society. In this paper, we confront the critical problem of epistemic miscalibration—where a model's linguistic assertiveness fails to reflect its true internal certainty. We introduce a new human-labeled dataset and a novel method for measuring the linguistic assertiveness of Large Language Models which cuts error rates by over 50% relative to previous benchmarks. Validated across multiple datasets, our method reveals a stark misalignment between how confidently models linguistically present information and their actual accuracy. Further human evaluations confirm the severity of this miscalibration. This evidence underscores the urgent risk of the overstated certainty Large Language Models hold which may mislead users on a massive scale. Our framework provides a crucial step forward in diagnosing and correcting this miscalibration, offering a path to safer and more trustworthy AI across domains.
Simulation System Towards Solving Societal-Scale Manipulation
Maximilian Puelma Touzel
Sneheel Sarangi
Austin Welch
Gayatri K
Dan Zhao
Zachary Yang
Hao Yu
Tom Gibbs
Ethan Kosak-Hine
Andreea Musulan
Camille Thibault
Busra Tugce Gurbuz
Kellin Pelrine
The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-w… (voir plus)orld settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. Through a variety of means we then improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys of the agents' political positions. We demonstrate the simulator with a tailored example of how partisan manipulation of agents can affect election results.
Simulation System Towards Solving Societal-Scale Manipulation
Maximilian Puelma Touzel
Sneheel Sarangi
Austin Welch
Gayatri K
Dan Zhao
Zachary Yang
Hao Yu
Tom Gibbs
Ethan Kosak-Hine
Andreea Musulan
Camille Thibault
Busra Tugce Gurbuz
Kellin Pelrine
The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-w… (voir plus)orld settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. Through a variety of means we then improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys of the agents' political positions. We demonstrate the simulator with a tailored example of how partisan manipulation of agents can affect election results.
Web Retrieval Agents for Evidence-Based Misinformation Detection
Jacob-Junqi Tian
Hao Yu
Yury Orlovskiy
Tyler Vergho
Mauricio Rivera
Mayank Goel
Zachary Yang
Kellin Pelrine
Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada
Zachary Yang
Anne Imouza
Maximilian Puelma Touzel
C'ecile Amadoro
Gabrielle Desrosiers-Brisebois
Kellin Pelrine
Sacha Lévy
Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded … (voir plus)specific events, such as when and which particular interventions came into practise. In this work, we develop and apply an approach to measure subnational and event-driven variation of partisan polarization and explore how these dynamics varied both across and within countries. We apply our measure to a dataset of over 50 million tweets posted during late 2020, a salient period of polarizing discourse in the early phase of the pandemic. In particular, we examine regional variations in both the United States and Canada, focusing on three specific health interventions: lockdowns, masks, and vaccines. We find that more politically conservative regions had higher levels of partisan polarization in both countries, especially in the US where a strong negative correlation exists between regional vaccination rates and degree of polarization in vaccine related discussions. We then analyze the timing, context, and profile of spikes in polarization, linking them to specific events discussed on social media across different regions in both countries. These typically last only a few days in duration, suggesting that online discussions reflect and could even drive changes in public opinion, which in the context of pandemic response impacts public health outcomes across different regions and over time.
Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada
Zachary Yang
Anne Imouza
Maximilian Puelma Touzel
C'ecile Amadoro
Gabrielle Desrosiers-Brisebois
Kellin Pelrine
Sacha Lévy
Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded … (voir plus)specific events, such as when and which particular interventions came into practise. In this work, we develop and apply an approach to measure subnational and event-driven variation of partisan polarization and explore how these dynamics varied both across and within countries. We apply our measure to a dataset of over 50 million tweets posted during late 2020, a salient period of polarizing discourse in the early phase of the pandemic. In particular, we examine regional variations in both the United States and Canada, focusing on three specific health interventions: lockdowns, masks, and vaccines. We find that more politically conservative regions had higher levels of partisan polarization in both countries, especially in the US where a strong negative correlation exists between regional vaccination rates and degree of polarization in vaccine related discussions. We then analyze the timing, context, and profile of spikes in polarization, linking them to specific events discussed on social media across different regions in both countries. These typically last only a few days in duration, suggesting that online discussions reflect and could even drive changes in public opinion, which in the context of pandemic response impacts public health outcomes across different regions and over time.
Political Dynasties in Canada
Alex B. Rivard
Marc André Bodet
Using a unique dataset of legislators' electoral and biographical data in the Canadian provinces of Ontario, Quebec, New Brunswick, Nova Sco… (voir plus)tia and the federal parliament, this article analyses the extent to which family dynasties affected the career development of legislators since the mid-18th century. We find that the prevalence of dynasties was higher in provincial legislatures than it was in the federal parliament, that the number of dynasties in the Senate increased until the mid-20th century, and that the proportion of dynastic legislators at the subnational level was similar to the numbers seen in the United Kingdom during the early 19th century. Our results confirm the existence of a clear career benefit in terms of cabinet and senate appointments. In contrast to the American case and in line with the United Kingdom experience, we find no causal relationship between a legislator's tenure length and the presence of a dynasty.