How Malicious AI Swarms Can Threaten Democracy
- URL: http://arxiv.org/abs/2506.06299v2
- Date: Tue, 10 Jun 2025 08:42:37 GMT
- Title: How Malicious AI Swarms Can Threaten Democracy
- Authors: Daniel Thilo Schroeder, Meeyoung Cha, Andrea Baronchelli, Nick Bostrom, Nicholas A. Christakis, David Garcia, Amit Goldenberg, Yara Kyrychenko, Kevin Leyton-Brown, Nina Lutz, Gary Marcus, Filippo Menczer, Gordon Pennycook, David G. Rand, Frank Schweitzer, Christopher Summerfield, Audrey Tang, Jay Van Bavel, Sander van der Linden, Dawn Song, Jonas R. Kunst,
- Abstract summary: Malicious AI swarms can coordinate covertly, infiltrate communities, evade traditional detectors, and run continuous A/B tests.<n>The result can include fabricated grassroots consensus, fragmented shared reality, mass harassment, voter micro-suppression or mobilization.<n>We urge a three-pronged response: always-on swarm-detection dashboards, pre-election high-fidelity swarm-simulation stress-tests, transparency audits, and optional client-side "AI shields" for users.
- Score: 42.60750455396757
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Advances in AI portend a new era of sophisticated disinformation operations. While individual AI systems already create convincing -- and at times misleading -- information, an imminent development is the emergence of malicious AI swarms. These systems can coordinate covertly, infiltrate communities, evade traditional detectors, and run continuous A/B tests, with round-the-clock persistence. The result can include fabricated grassroots consensus, fragmented shared reality, mass harassment, voter micro-suppression or mobilization, contamination of AI training data, and erosion of institutional trust. With democratic processes worldwide increasingly vulnerable, we urge a three-pronged response: (1) platform-side defenses -- always-on swarm-detection dashboards, pre-election high-fidelity swarm-simulation stress-tests, transparency audits, and optional client-side "AI shields" for users; (2) model-side safeguards -- standardized persuasion-risk tests, provenance-authenticating passkeys, and watermarking; and (3) system-level oversight -- a UN-backed AI Influence Observatory.
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