Evolving AI Collectives to Enhance Human Diversity and Enable
  Self-Regulation
Shiyang Lai Yujin Potter Junsol Kim Richard Zhuang Dawn Song James Evans
Large language models steer their behaviors based on texts generated by
others. This capacity and their increasing prevalence in online settings
portend that they will intentionally or unintentionally "program" one another
and form emergent AI subjectivities, relationships, and collectives. Here, we
call upon the research community to investigate these "society-like" properties
of interacting artificial intelligences to increase their rewards and reduce
their risks for human society and the health of online environments. We use a
simple model and its outputs to illustrate how such emergent, decentralized AI
collectives can expand the bounds of human diversity and reduce the risk of
toxic, anti-social behavior online. Finally, we discuss opportunities for AI
self-moderation and address ethical issues and design challenges associated
with creating and maintaining decentralized AI collectives.