Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation

Shiyang Lai, Yujin Potter, Junsol Kim, Richard Zhuang, Dawn Song, James Evans
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:25892-25912, 2024.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-lai24b, title = {Position: Evolving {AI} Collectives Enhance Human Diversity and Enable Self-Regulation}, author = {Lai, Shiyang and Potter, Yujin and Kim, Junsol and Zhuang, Richard and Song, Dawn and Evans, James}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {25892--25912}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/lai24b/lai24b.pdf}, url = {https://proceedings.mlr.press/v235/lai24b.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation %A Shiyang Lai %A Yujin Potter %A Junsol Kim %A Richard Zhuang %A Dawn Song %A James Evans %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-lai24b %I PMLR %P 25892--25912 %U https://proceedings.mlr.press/v235/lai24b.html %V 235 %X 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.
APA
Lai, S., Potter, Y., Kim, J., Zhuang, R., Song, D. & Evans, J.. (2024). Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:25892-25912 Available from https://proceedings.mlr.press/v235/lai24b.html.

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