A Simulation System Towards Solving Societal-Scale Manipulation
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arXiv
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| Format: | Preprint |
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2024
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| author | Touzel, Maximilian Puelma Sarangi, Sneheel Welch, Austin Krishnakumar, Gayatri Zhao, Dan Yang, Zachary Yu, Hao Kosak-Hine, Ethan Gibbs, Tom Musulan, Andreea Thibault, Camille Gurbuz, Busra Tugce Rabbany, Reihaneh Godbout, Jean-François Pelrine, Kellin |
| author_facet | Touzel, Maximilian Puelma Sarangi, Sneheel Welch, Austin Krishnakumar, Gayatri Zhao, Dan Yang, Zachary Yu, Hao Kosak-Hine, Ethan Gibbs, Tom Musulan, Andreea Thibault, Camille Gurbuz, Busra Tugce Rabbany, Reihaneh Godbout, Jean-François Pelrine, Kellin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13915 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Simulation System Towards Solving Societal-Scale Manipulation Touzel, Maximilian Puelma Sarangi, Sneheel Welch, Austin Krishnakumar, Gayatri Zhao, Dan Yang, Zachary Yu, Hao Kosak-Hine, Ethan Gibbs, Tom Musulan, Andreea Thibault, Camille Gurbuz, Busra Tugce Rabbany, Reihaneh Godbout, Jean-François Pelrine, Kellin Social and Information Networks Artificial Intelligence Computers and Society 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. |
| title | A Simulation System Towards Solving Societal-Scale Manipulation |
| topic | Social and Information Networks Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2410.13915 |