Johnson Neil F, Sear Richard, Illari Lucia
Dynamic Online Networks Laboratory, George Washington University, Washington, DC 20052, USA.
PNAS Nexus. 2024 Jan 23;3(1):pgae004. doi: 10.1093/pnasnexus/pgae004. eCollection 2024 Jan.
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.
我们考虑到恶意行为者利用人工智能（AI）/生成式预训练变换器在全球社交媒体上造成危害的潜在威胁。在我们对在线多平台战场的详细映射的指导下，我们回答了关键问题：哪些恶意行为者的人工智能活动可能占主导地位、在何处、何时发生，以及可以采取哪些措施来大规模控制它。应用先前对网络和自动算法攻击研究中的动态红皇后分析，预测到2024年年中，恶意行为者的人工智能活动将升级为每日发生，就在美国和其他全球选举之前。然后，我们使用观察到的恶意行为者社区聚类动态的精确可解数学模型，构建一个政策矩阵，该矩阵量化了两个潜在理想结果之间的结果和权衡：遏制未来恶意行为者的人工智能活动与彻底消除它。我们还给出了相关风险度量的明确即插即用公式。
