On the transparency of large AI models

Abstract

As large AI models demonstrate increasingly human-like performance on complex tasks, many scientists are developing or adapting these models to empower their research and applications. Because of the substantial costs involved in building, training, and running large AI models, closedsource models can often offer performance that cannot be matched by open-source counterparts, making them tempting tools for researchers even if they are not transparent or accessible according to conventional academic standards. Moreover, even researchers who are developing their own AI models may face special challenges when trying to publish their work in an open and reproducible manner. In particular, the very large datasets required to train AI models often come with special challenges that make them inherently hard to share—ranging from sheer size to tricky copyright and privacy issues. In this editorial, we share some insights and tips that we hope will help researchers in this field understand our journal’s policies and prepare submissions for the journal.