Part of the Deep Dive: AI Webinar Series
Open source has always found its legal foundation primarily in copyright. Although many codes of behavior around open source have been adopted and promulgated by various open source communities, in the end it is the license attached to any piece of open source that dictates how it may be used and what obligations a user must abide by in order to remain legally compliant.
Artificial Intelligence is raising, and will continue to raise, profound questions about how copyright law applies — or does not apply — to the process of ingesting training content, processing that content to extract information used to generate output, what the that information is, and the nature of the output produced.
Much debate, and quite a bit of litigation, has recently been generated around questions raised by the input phase of training Artificial Intelligence, and to what extent the creators of materials used in that input phase have any right — morally or legally — to object to that training. At the same time, whether or not the output of AI can be the subject matter of copyright, or patent, protection is also being tested in various jurisdictions — with clashing results. What occurs between input and output remains an unresolved issue — and whether there is any legal regime that can be used to guarantee that legal, normative rules can control how those processes are used exist in the way that copyright, and copyright licensing, do so in open source at present.
The presentation will discuss these issues in depth with a lens toward testing whether copyright — or any other intellectual property regime — really can be useful in keeping AI “open.”
In this webinar hosted by the Open Source Initiative as a part of the “Deep Dive: Defining Open Source AI” series, McCoy Smith provides an overview of open source licensing, copyright, and its application to artificial intelligence (AI) systems. McCoy discusses the history of open source licensing and how it has primarily relied on copyright law. He explores the challenges and debates surrounding AI systems and copyright, especially concerning training data, weights, and vectors generated by these systems. McCoy speculates on potential future developments, including legislative solutions, contract-based approaches, and public pledges as means to address the evolving landscape of open source AI. Finally, he highlights the complexities and legal ambiguities arising from the interplay of copyright law and AI technology.