Sep

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

    1 PM EDT

    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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    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.

    Event Details

    Duration

    90

    Format

    Web

    Date

    Sep 22, 2020

    2020-09-22T13:00:00-04:00 2020-09-22T14:30:00-04:00 Landslide: IP Legal Pitfalls of Machine Learning Systems [CC] 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.

    Sponsors

    Section of Intellectual Property Law

    Panelists

    Nicola Menaldo

    Roger Bodamer

    Steven Carlson

    MCLE Information

    The ABA will seek 1.50 general CLE credit hours in 60-minute-hour states, and 1.80 general CLE credit hours in 50-minute states. Credit hours are estimated and are subject to each state’s approval and credit rounding rules.

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