Member-benefit exclusive!  A presentation from the Front & Center Discussion Series. No CLE credit available. 

Big data is the primary asset for many businesses. Using artificial intelligence (AI), data can drive new revenue streams, new customer insights, and new product enhancements. Given the massive value that AI can create, protecting the big data that is fed into AI models is critical for data-driven businesses. This session will provide attendees with the opportunity to learn about the various strategies for legal protection of data sets under U.S. law, including the benefits and challenges of different IP frameworks. Additionally, this session will compare domestic data protection laws with international data protection approaches. Participants will also gain a better understanding of how data is being used to drive American businesses and the strategic choices that such businesses are considering to protect their data. Finally, this session will include insight into the Open Data movement, in which open source software principles are applied to the collection and distribution of big data.

System requirements: 

Webinar access is compatible with any Windows 7 or later computer, Android OS devices, or Apple/iOS devices.  Check system compatibility here.

Accessibility for hearing impaired:

AIPLA’s webinars are available and accessible to individuals who are hearing impaired. If anyone at your location would like to know more about accommodations, please contact cle@aipla.org. We ask that you let us know at least 7 business days out from the webinar, to ensure that we can identify and deploy the solution that best fits our registrants needs.

 Disclaimer:  AIPLA is a nonprofit national bar association.  The sole purpose of this CLE program is to provide educational and informational content.  AIPLA does not provide legal services or advice.  The opinions, views and other statements expressed by contributors to this CLE program are solely those of the contributors.  These opinions, views and statements of the contributors do not necessarily represent those of AIPLA and should not be construed as such.

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