53 min
Tags, Art, and AI. Oh My. MCN 2019 sessions recordings
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- Education
Wednesday, November 6, 2019
In 2018, The Met added subject keyword tags to 300,000 artworks in their online collection. The goal of the project was to improve search and discovery of the collection, increase user engagement, and provide a new access point around depicted subject matter. The keyword tags have also opened up the collection to new types of exploration with artificial intelligence (AI).
The Met recently collaborated with the Wikimedia community using the keyword dataset to explore the use of AI around the museum’s collection. The keywords were connected to Wikidata terms which predicted tags for artworks the machine learning (ML) model had never seen. In many cases depicted subject matter was recognized with accuracy, but results were still mixed. With its global network of skilled volunteers, the Wikimedia community was able to add the additional element of human judgment in reviewing tags generated by AI.
An analysis of both machine and human-generated tags revealed issues around accuracy, completeness, relevance and bias, highlighting the challenges in describing subject matter depicted in art.
This session will examine the tagging process, discuss the collaboration with the Wikipedia community, and identify areas where AI models succeed and fail.
Session Type60-Minute Session (Professional Forum or Hands-on Demonstration)
TrackContent
Chatham House RuleNo
Key OutcomesThe session will provide insights about the opportunities and challenges of adding subject tags to museum collections. Participants will gain a better understanding of using AI for tag prediction and learn how human judgement is still an important factor in describing art.
Speakers
Session Leader : Jennie Choi, General Manager of Collection Information, The Metropolitan Museum of Art
Co-Presenter : Elena Villaespesa, Assistant Professor, Pratt Institute
Co-Presenter : Andrew Lih, Wikimedia Strategist, The Metropolitan Museum of Art, Digimentors Group
Wednesday, November 6, 2019
In 2018, The Met added subject keyword tags to 300,000 artworks in their online collection. The goal of the project was to improve search and discovery of the collection, increase user engagement, and provide a new access point around depicted subject matter. The keyword tags have also opened up the collection to new types of exploration with artificial intelligence (AI).
The Met recently collaborated with the Wikimedia community using the keyword dataset to explore the use of AI around the museum’s collection. The keywords were connected to Wikidata terms which predicted tags for artworks the machine learning (ML) model had never seen. In many cases depicted subject matter was recognized with accuracy, but results were still mixed. With its global network of skilled volunteers, the Wikimedia community was able to add the additional element of human judgment in reviewing tags generated by AI.
An analysis of both machine and human-generated tags revealed issues around accuracy, completeness, relevance and bias, highlighting the challenges in describing subject matter depicted in art.
This session will examine the tagging process, discuss the collaboration with the Wikipedia community, and identify areas where AI models succeed and fail.
Session Type60-Minute Session (Professional Forum or Hands-on Demonstration)
TrackContent
Chatham House RuleNo
Key OutcomesThe session will provide insights about the opportunities and challenges of adding subject tags to museum collections. Participants will gain a better understanding of using AI for tag prediction and learn how human judgement is still an important factor in describing art.
Speakers
Session Leader : Jennie Choi, General Manager of Collection Information, The Metropolitan Museum of Art
Co-Presenter : Elena Villaespesa, Assistant Professor, Pratt Institute
Co-Presenter : Andrew Lih, Wikimedia Strategist, The Metropolitan Museum of Art, Digimentors Group
53 min