在网络战场上大规模控制坏人人工智能活动.
Neil F Johnson1, Richard Sear1, Lucia Illari1
1Dynamic Online Networks Laboratory, George Washington University, Washington, DC 20052, USA.
PNAS nexus
|January 24, 2024
概括
到2024年中期,坏人可能会使用人工智能 (AI) 来对社交媒体造成日常伤害,从而威胁全球选举. 这项研究模拟了人工智能威胁,并量化了控制的政策权衡.
科学领域:
- 网络安全和人工智能 人工智能
- 社交媒体分析 社交媒体分析
- 计算社会科学 计算社会科学
背景情况:
- 人工智能 (AI) 的普及,特别是生成预训练的变压器,对全球社交媒体平台的恶意使用构成越来越大的威胁.
- 了解网络空间中不良行为者的行为动态对于开发有效的对策至关重要.
研究的目的:
- 预测社交媒体上人工智能驱动的恶意活动的性质,规模和时间.
- 开发一个框架来量化不同政策干预措施的有效性和对抗人工智能带来的伤害的权衡.
- 为控制大规模的人工智能产生的威胁提供可操作的见解.
主要方法:
- 动态红皇后分析根据网络和自动化算法攻击研究进行了调整.
- 观察到的坏演员社区集群动态的数学建模.
- 制定政策矩阵来量化干预结果.
主要成果:
- 预测到2024年中期,每日恶人AI活动的升级,与重大全球选举相吻合.
- 量化政策成果,包括控制与完全消除人工智能带来的伤害.
- 为相关风险措施提供明确的公式.
结论:
- 积极和战略性的政策干预是必要的,以减轻在社交媒体上不断升级的AI产生的伤害威胁.
- 该研究为管理AI相关安全风险的决策提供了定量基础.
- 及时实施控制措施至关重要,尤其是在即将到来的选举活动的背景下.
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