Portrait de Sneheel Sarangi

Sneheel Sarangi

Collaborateur·rice de recherche - McGill
Superviseur⋅e principal⋅e
Sujets de recherche
Alignement de l'IA
Apprentissage multimodal
Apprentissage par renforcement
Apprentissage profond
Généralisation hors distribution (OOD)
Grands modèles de langage (LLM)
IA responsable
Modèles génératifs
Raisonnement
Traitement du langage naturel

Publications

A Simulation System Towards Solving Societal-Scale Manipulation
Maximilian Puelma Touzel
Austin Welch
Gayatri Krishnakumar
Dan Zhao
Hao Yu
Ethan Kosak-Hine
Tom Gibbs
Busra Tugce Gurbuz
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
Austin Welch
Gayatri K
Dan Zhao
Hao Yu
Ethan Kosak-Hine
Tom Gibbs
Busra Tugce Gurbuz
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.
Simulation System Towards Solving Societal-Scale Manipulation
Maximilian Puelma Touzel
Austin Welch
Gayatri K
Dan Zhao
Hao Yu
Tom Gibbs
Ethan Kosak-Hine
Busra Tugce Gurbuz
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
Austin Welch
Gayatri K
Dan Zhao
Hao Yu
Tom Gibbs
Ethan Kosak-Hine
Busra Tugce Gurbuz
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.