Portrait of Sneheel Sarangi

Sneheel Sarangi

Collaborating researcher - McGill University
Supervisor
Research Topics
AI Alignment
Deep Learning
Generative Models
Large Language Models (LLM)
Multimodal Learning
Natural Language Processing
Out-of-Distribution (OOD) Generalization
Reasoning
Reinforcement Learning
Responsible AI

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… (see more)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… (see more)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… (see more)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… (see more)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.