Landslide: IP Legal Pitfalls of Machine Learning Systems [CC]

Landslide: IP Legal Pitfalls of Machine Learning Systems [CC] — CLE 90 min

Machine learning systems require ingesting massive amounts of data. There are often legal restrictions tied to the ingested data, such as trade secret, copyright, misuse, and/or privacy restrictions. Failure to understand and control for these risks can expose the developer to uncurable legal liability.

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12/31/2022

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03/21/2022

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Machine learning systems have a fundamental vulnerability from a legal perspective: once information is incorporated into a machine learning model, there is virtually no way to remove it. If ingested data are tainted legally—for example via trade secret misappropriation, violation of license restrictions, copyright violation—there may be limited or no practical ways to expunge the offending data from the model. This webinar panel explores the intellectual property issues and concerns surrounding machine learning systems. The panel provides recommendations and preventative guidelines to avoid the legal pitfalls.

Panelists

Nicola Menaldo, Roger Bodamer, Steven Carlson

Sponsors

Section of Intellectual Property Law

Product Code

PT2009LSMOLC

Duration

90

Publication Date

9/1/2020 12:00:00 AM

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