We show how the looming threat of bad actors using AI/GPT to generate harms across social media, can be addressed at scale by exploiting the intrinsic dynamics of the social media multiverse. We combine a uniquely detailed description of the current bad-actor-mainstream battlefield with a mathematical description of its behavior, to show what bad-actor-AI activity will likely dominate, where, and when. A dynamical Red Queen analysis predicts an escalation to daily bad-actor-AI activity by early 2024, just ahead of U.S. and other global elections. We provide a Policy Matrix that quantifies outcomes and trade-offs mathematically for the policy options of containment vs. removal. We give explicit plug-and-play formulae for risk measures.
Cite
@article{arxiv.2308.00879,
title = {Controlling bad-actor-AI activity at scale across online battlefields},
author = {Neil F. Johnson and Richard Sear and Lucia Illari},
journal= {arXiv preprint arXiv:2308.00879},
year = {2023}
}