# AI Contagion in Social Networks

arXiv: https://arxiv.org/abs/2606.15206 · HTML: https://mlsanity.com/paper/2606.15206/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 13 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Predictable Artificial Intelligence](https://mlsanity.com/paper/2310.06167/) (2023)
- [Network Structure and Naive Sequential Learning](https://mlsanity.com/paper/1703.02105/) (2017)
- [AI Behavioral Science](https://mlsanity.com/paper/2509.13323/) (2025)
- [How AI Aggregation Affects Knowledge](https://mlsanity.com/paper/2604.04906/) (2026)

## Abstract (arXiv)

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.