Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation
<|reference_start|>Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation: Large language model behavior is shaped by the language of those with whom they interact. This capacity and their increasing prevalence online 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 "societies" of interacting artificial intelligences to increase their rewards and reduce their risks for human society and the health of online environments. We use a small "community" of models and their evolving outputs to illustrate how such emergent, decentralized AI collectives can spontaneously expand the bounds of human diversity and reduce the risk of toxic, anti-social behavior online. Finally, we discuss opportunities for AI cross-moderation and address ethical issues and design challenges associated with creating and maintaining free-formed AI collectives.<|reference_end|>
@article{lai2024evolving,
    title={Evolving AI Collectives to Enhance Human Diversity and Enable
  Self-Regulation},
    author={Shiyang Lai, Yujin Potter, Junsol Kim, Richard Zhuang, Dawn Song,
  James Evans},
    journal={arXiv preprint arXiv:2402.12590},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12590},
    primaryClass={cs.CL cs.CY}
}