Katherine Elkins — accepted AI-conference papers

2026

Position: If open source is to win, it must go public

Joshua Tan, Nicholas Vincent, Katherine Elkins, Magnus Sahlgren, Joseph Low, David Pham +2

ICML 2026spotlight

Open source projects have made incredible progress in producing widely usable machine learning models and systems, but open source alone will face challenges in fully democratizing access to AI. Unlike previous generations of open source software, open source and open weight AI models require substa…

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2024

Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI

Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder de Witt, Fabio Pizzati, Katherine Elkins +18

ICML 2024oral

In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks and resulted in calls for tighter regulation, in p…

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