Legal issues of AI in research

Legal issues of AI in research

Best Practices for AI Results, Compliance & Licensing

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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.

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KU Leuven, Belgium

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University of Vienna, Austria

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Centre for research in agricultural Genomics, Spain

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Palacký University Olomouc, Czech Republic

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Radboud University, Netherlands