arXiv:2606.15206v1 Announce Type: cross Abstract: We study how artificial intelligence (AI) interacts with social communication networks to shape the stability of collective knowledge. Agents exchange information through a network while receiving AI-generated content, and AI systems retrain on the aggregate social information they influence. This interaction generates two feedback forces: an AI contagion channel, through which distortions diffuse across the network, and an AI social distortion multiplier, through which retraining amplifies past errors. Despite the high dimensionality of the en
The proliferation of AI-generated content and increasingly sophisticated AI models necessitates a deeper understanding of their interaction with social networks and their potential for systemic impact.
This research highlights critical feedback loops where AI both influences and is influenced by social information, leading to new forms of information distortion and systemic risk for collective knowledge and decision-making.
Our understanding of AI's role in information environments expands from simple generation to complex, self-amplifying distortions within social communication, moving beyond passive influence to active contagion.
- · Resilience researchers
- · Information integrity platforms
- · Ethical AI developers
- · Social networks without robust moderation
- · Individuals susceptible to misinformation
- · AI systems without 'truth' grounding
AI-generated content will increasingly shape and potentially destabilize social communication and collective knowledge.
Governments and regulatory bodies will face increased pressure to develop frameworks addressing algorithmic contagion and amplification of falsehoods.
The concept of 'truth' and consensus in a highly interconnected, AI-influenced world could become fundamentally altered, impacting democratic processes and societal cohesion.
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Read at arXiv cs.AI