Jean-François Godbout

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.

2025-03-05

ICLR.cc/2025/Workshop/AI4CHL (présentation orale)

A Guide to Misinformation Detection Data and Evaluation

Camille Thibault

Jacob-Junqi Tian

Gabrielle Péloquin-Skulski

Taylor Lynn Curtis

Florence Laflamme

James Zhou

Yuxiang Guan

Kellin Pelrine

Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this probl… (voir plus)em, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of the 36 datasets that consist of statements or claims, as well as the 9 datasets that consists of data in purely paragraph form. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as insufficient label quality, spurious correlations. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. We discuss alternatives to mitigate this problem. Overall, this guide aims to provide a roadmap for obtaining higher quality data and conducting more effective evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at [anonymized].

2025-03-05

ICLR.cc/2025/Workshop/MLDPR (publié)

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.

2024-12-18

ArXiv (prépublication)

A Guide to Misinformation Detection Data and Evaluation

Camille Thibault

Jacob-Junqi Tian

Gabrielle Péloquin-Skulski

Taylor Lynn Curtis

James Zhou

Florence Laflamme

Yuxiang Guan

Kellin Pelrine

2024-11-07

ArXiv (prépublication)

A Simulation System Towards Solving Societal-Scale Manipulation

Maximilian Puelma Touzel

Sneheel Sarangi

Austin Welch

Gayatri K

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.

2024-10-17

ArXiv (prépublication)

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.

2024-10-17

ArXiv (prépublication)

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.

2024-10-12

NeurIPS.cc/2024/Workshop/SafeGenAi (poster)

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.

2024-10-12

NeurIPS.cc/2024/Workshop/SafeGenAi (poster)

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.

2024-10-12

NeurIPS.cc/2024/Workshop/SafeGenAi (poster)

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.

2024-10-12

NeurIPS.cc/2024/Workshop/SafeGenAi (poster)

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

2024-07-10

colmweb.org/COLM/2024/Conference (accepté)

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.

2024-07-03

ArXiv (prépublication)