Controlling bad-Actor-Artificial intelligence activity at scale across online battlefields

Abstract

We consider the looming threat of bad actors using artificial intelligence (AI)/Generative Pretrained Transformer to generate harms across social media globally. Guided by our detailed mapping of the online multiplatform battlefield, we offer answers to the key questions of what bad-Actor-AI activity will likely dominate, where, when-And what might be done to control it at scale. Applying a dynamical Red Queen analysis from prior studies of cyber and automated algorithm attacks, predicts an escalation to daily bad-Actor-AI activity by mid-2024-just ahead of United States and other global elections. We then use an exactly solvable mathematical model of the observed bad-Actor community clustering dynamics, to build a Policy Matrix which quantifies the outcomes and trade-offs between two potentially desirable outcomes: containment of future bad-Actor-AI activity vs. its complete removal. We also give explicit plug-And-play formulae for associated risk measures.