This paper explores the concept of openness in artificial intelligence (AI), including relevant terminology and how different degrees of openness can exist. It explains why the term “open source” – a term rooted in software – does not fully capture the complexities specific to AI. This paper analyses current trends in open-weight foundation models using experimental data, illustrating both their potential benefits and associated risks. It incorporates the concept of marginality to further inform this discussion. By presenting information clearly and concisely, the paper seeks to support policy discussions on how to balance the openness of generative AI foundation models with responsible governance.
Gary Price (gprice@gmail.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area.
He earned his MLIS degree from Wayne State University in Detroit.
Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com.
