Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations

TL;DR

This paper examines how advanced generative language models could enable malicious influence operations online, analyzes potential future threats, and discusses mitigation strategies across different stages of influence campaigns.

Contribution

It provides a comprehensive framework for understanding the impact of language models on influence operations and proposes mitigation approaches targeting various stages of the process.

Findings

Language models can automate convincing influence content

Multiple mitigation strategies can reduce threat impact

No single solution fully prevents AI-enabled influence operations

Abstract

Generative language models have improved drastically, and can now produce realistic text outputs that are difficult to distinguish from human-written content. For malicious actors, these language models bring the promise of automating the creation of convincing and misleading text for use in influence operations. This report assesses how language models might change influence operations in the future, and what steps can be taken to mitigate this threat. We lay out possible changes to the actors, behaviors, and content of online influence operations, and provide a framework for stages of the language model-to-influence operations pipeline that mitigations could target (model construction, model access, content dissemination, and belief formation). While no reasonable mitigation can be expected to fully prevent the threat of AI-enabled influence operations, a combination of multiple mitigations may make an important difference.