AI Contagion in Social Networks
Olivier Bos, Stefano Bosi
Why It Matters
What makes this one worth your time
Understanding AI's impact on social networks is crucial for developing effective regulatory measures to mitigate misinformation and enhance collective knowledge stability.
This research explores the destabilizing effects of AI on social networks and proposes a regulatory approach for stability.
Summary
The paper investigates the interaction between AI and social communication networks, focusing on how AI-generated content influences the stability of collective knowledge through feedback mechanisms, ultimately proposing a regulatory framework for stability.
Key contributions
- Characterization of feedback mechanisms between AI and social networks.
- Development of a regulatory frontier for stability in AI-mediated information systems.
- Analysis of how network topology influences systemic informational risk.
Notable insights
- The identification of an AI contagion channel and a social distortion multiplier provides a novel perspective on the dynamics of information spread in networks.
- The use of a two-dimensional representation to analyze the system's long-run behavior is an interesting methodological approach.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2606.15206v2 Announce Type: replace-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 AI systems generate content and retrain on the aggregate informational environment they influence. This interaction creates a recursive feedback loop in which informational distortions diffuse through society and subsequently feed back into future AI outputs. Despite the high dimensionality of the environment, we show that the long-run dynamics admit a two-dimensional representation whose spectral radius completely characterizes the stability of AI-mediated information systems. We derive a sharp regulatory frontier identifying the minimum filtering required for stability and show how homophily and core-periphery network structures shape systemic informational risk.