FATHOM II
Four panel discussions, one registration
Watch the panel discussions
Four experts in four online conversations will further dissect the issues posed by AI systems to the Open Source communities and society as a whole.
Business
Explore the business models in AI, what’s foreseeable to collaborate on datasets, models and other artifacts of AI/ML. What should be the rules of engagement for fair competition, what areas are the most challenging, what experience from the Open Source movement can be taken to work on AI components, what challenges and limitations can be foreseen.
Astor Nummelin Carlberg
Executive Director, Open Forum Europe
Sal Kimmich
Developer Relations - Open Source
Alek Tarkowski
Director of Strategy at Open Future Foundation
David Kanter
Executive Director of MLCommons
Stella Biderman
EleutherAI
Society
AI impacts everyday life. Open Source software is supposed to help generate local economies, distribute knowledge, reduce dependence on foreign corporations, stimulate competition and prevent monopolies. What can we do to balance the power of corporations in AI? How can the society as a whole maintain control of AI systems?
Kat Walsh
General Counsel, Creative Commons
Carlos Muñoz Ferrandis
AI Counsel, Hugging Face
Luis Villa
Co-founder and General Counsel, Tidelift
Kit Walsh
Senior Staff Attorney and Assistant Director, Electronic Frontier Foundation
Legal
A focus on legal aspects, from what is copyrightable as output of AI (the images produced by DALL-E or the code snippets from Copilot) in the current legal systems. We’ll better understand concepts like the right to data mining, what are the gray areas for redistribution of training datasets, pre-trained models and other artifacts of AI.
Pamela Chestek
Principal, Chestek Legal
Danish Contractor
Researcher, IBM Research
Jennifer Lee
Technology and Liberty Project Manager, ACLU-WA
Adrin Jalali
Maintainer, Scikit-learn and Fairlearn, Hugging Face
Academia
Open Source software started in academic circles, and AI is not different. How are academics sharing datasets and models? What do they need to be able to replicate experiments and improve on their knowledge? What legal obstacles do they find? What social norms prevent collaboration?
Chris Albon
Director of Machine Learning, Wikimedia Foundation
Mark Surman
Executive Director, Mozilla Foundation
Ibrahim Haddad, Ph.D.
General Manager, LF AI & Data Foundation
Amy Heineike
VP Engineering, 7bridges
