Poster
in
Workshop: Socially Responsible Language Modelling Research (SoLaR)
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 · Reihaneh Rabbany · Jean-François Godbout · Kellin Pelrine
Keywords: [ llm ] [ generative agents ] [ disinformation ] [ simulation ] [ manipulation ] [ ai safety ]
The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-world 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.