Research groups are embedding AI into research process and results. Examples include (a) creating new AI models as a research result, using existing or new models and datasets (b) embedding existing AI models or systems into a new result (c) scraping or otherwise obtaining data (licensing, online publication, etc.) for (re) training AI models in research labs (d) creating solutions (platforms, APIs, libraries) that leverage third party AI services such as OpenAI into research results (e) using AI to create code, images, text, data and other results (in part, hopefully, with human supervision afterwards).
In the KT process, this usage can give rise to significant legal issues that need to be understood and managed by the KTO: regulatory compliance (data and AI), ownership and IP protection of results, licensing in conditions, exploitation models.
This masterclass reviews the different scenarios and presents state of the art and best practices in terms of protection and management of results, regulatory compliance and outbound licensing when AI is involved.
Good opportunity to discuss common issues with fellow professionals and to get updates about the community.
Giuseppe Visimberga
Leiden University, Netherlands
Working and Learning with old and new friends – best way to do it!
Sara Matt-Leubner
University of Innsbruck, Austria
Great atmosphere to learn and to share experiences.
Maria Ruiz Novales
Interface-Enterprises - University of Liege, Belgium
From day one ASTP has been a tremendous help. Learning throughs seminars and workshops or meeting peers for discussions, there is always someone around to exchange ideas with.
Paul van Dun
KU Leuven, Belgium
The software course has provided invaluable information as our TTO expands to take on software cases.
Tom Withnell
University of Vienna, Austria
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