Abstract
As Large Language Models (LLMs) continue to grow and evolve, the ethical, social, and legal challenges they present, especially in terms of data use and sharing, come sharply into focus. Particularly in high-risk domains like healthcare, where the sensitivity of data is pronounced, and the ramifications of data leakages or misuse are profound. These licenses, envisioned to address the complexities introduced by LLMs, still grapple with barriers that hinder their full potential: notably, the absence of authoritative regulatory bodies, ambiguities in defining what constitutes 'sufficient public benefit', issues of data sovereignty, and dilemmas surrounding responsibility and liability. Herein, we delve into these intricacies, focusing on the evolution of social licenses, their relationship with prevailing governance model challenges, and the essential transformations required to ensure their safe and responsible deployment in the rapidly evolving world of LLMs.
Recommended Citation
Pierce, Robin L.; Gallifant, Jack; Cordes, Ashley; Fiske, Amelia; Dorotic, Matilda; Lyndon, Mataroria; Jain, Shrey; Zhang, Joe; Gichoya, Judy; and Celi, Leo Anthony
(2024)
"Defining the Social Licence of Large Language Models in Healthcare,"
Seattle Journal of Technology, Environmental, & Innovation Law: Vol. 15:
Iss.
1, Article 6.
Available at:
https://digitalcommons.law.seattleu.edu/sjteil/vol15/iss1/6