Controlling bad-actor-artificial intelligence activity at scale across online battlefields.
PNAS Nexus
; 3(1): pgae004, 2024 Jan.
Article
em En
| MEDLINE
| ID: mdl-38264146
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article