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Bad actors will likely use artificial intelligence (AI) for daily social media harms by mid-2024, threatening global elections. This study models AI threats and quantifies policy trade-offs for control.

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Area of Science:

  • Cybersecurity and Artificial Intelligence
  • Social Media Analysis
  • Computational Social Science

Background:

  • The proliferation of artificial intelligence (AI), particularly Generative Pretrained Transformers, presents a growing threat of malicious use on global social media platforms.
  • Understanding the dynamics of bad actor behavior in the online space is crucial for developing effective countermeasures.

Purpose of the Study:

  • To predict the nature, scale, and timing of AI-driven malicious activities on social media.
  • To develop a framework for quantifying the effectiveness and trade-offs of different policy interventions against AI-enabled harms.
  • To provide actionable insights for controlling AI-generated threats at scale.

Main Methods:

  • Dynamical Red Queen analysis adapted from cyber and automated algorithm attack studies.
  • Mathematical modeling of observed bad actor community clustering dynamics.
  • Development of a Policy Matrix to quantify intervention outcomes.

Main Results:

  • Prediction of escalating daily bad actor AI activity by mid-2024, coinciding with major global elections.
  • Quantification of policy outcomes, including containment versus complete removal of AI-driven harms.
  • Provision of explicit formulae for associated risk measures.

Conclusions:

  • Proactive and strategic policy interventions are necessary to mitigate the escalating threat of AI-generated harms on social media.
  • The study provides a quantitative basis for decision-making in managing AI-related security risks.
  • Timely implementation of control measures is critical, especially in the context of upcoming electoral events.