The Importance of Metadata in AI
Scope
Metadata provides critical context and information about the data being used to train and operate AI algorithms. It is the framework that shapes how AI systems process, interpret, and generate insights from available data in order to respond to queries. Through the presentation of case studies and discussion, this program will investigate the impact of metadata on algorithms, ethical considerations, and transparency.
Confirmed speakers include Karim Boughida, Dean, Stony Brook University Libraries, Paul Groth, Professor of Algorithmic Data Science, University of Amsterdam, Helen King, Independent Technology Strategy Consultant, and Hong Zhou, Director of Intelligent Services Group & Head of AI R&D, Wiley Partner Solutions.
The program is being coordinated by NISO Education Committee Member Lieke Boerefijn - Van Schaaijk '21, Platform Manager, CABI.
Event Sessions
Speakers
This event was moderated by Karim Boughida, Dean of University Libraries, Stony Brook University.
In anticipation of this event, the following questions were posed to the panel as a basis for discussion…
Is metadata “meaningful” in AI? How so?
How does the way that AI uses metadata change the way metadata is utilized? How does AI technically use metadata?
Where does metadata have the most value in AI?
With regards to images - What is the “problem” they create
What is the importance of provenance (tracing) and clarifying authenticity
What skills are most important as a result of GenAI and how does the “value chain of information” and metadata assist information service professionals
Discuss Content Authenticity Metadata Standards (Adobe, Microsoft, etc.) and the Provenance lifecycle
What impacts will there be for metadata roles and jobs - are they at risk and how will they change?
How will AI be generating metadata and how will that be monitored?
What consensus is needed to ensure that the metadata generated is accurate and appropriate?
If AI is an amplifier, it will capture and enhance biases (culturally appropriate, as well as negatively), how does that get managed and contextualized and tracked?
Where are the gaps? How do we keep up and maintain the level of quality and trustworthiness? How can libraries work toward metadata literacy?
How can legacy metadata be protected?
Do you need traditional academic metadata in a Generative AI world?
Prediction: How do you see publishers using AI in metadata and what are the changes you predict to take place over the next 5 years?
Related Information and Shared Resources:
https://nanodash.petapico.org/ - a project currently investigating blockchain and provenance. Nanodash is a client to browse and publish nanopublications.
https://contentauthenticity.org/ - Restoring trust and transparency in the age of AI
https://www.niso.org/standards-committees/cpld - NISO Content Profile/Linked Document [CP/LD] Working Group
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Event Dates
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Location
Timing shown for this event is Eastern Daylight Time. All NISO events follow the practices of the Eastern Time Zone (US & Canada).