GLAM Wiki 2025/Program/AI and the Cultural Commons
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| Date & Time | October 31, 16:30-17:45 |
| Room | Room 1 |
| Language | EN |
| Speakers | Ben Vershbow has worked at the intersection of culture and technology for over 20 years in a varied career spanning the Institute for the Future of the Book, the New York Public Library (NYPL Labs), and most recently, the Wikimedia Foundation. Ben recently joined the GLAM-E Lab as a Research Fellow focused on lowering technical barriers to GLAM institutions seeking to share open collections effectively into the internet ecosystem. He’s in the early stages of incubating a new venture focused on strengthening the software layer between open cultural collections and platforms like Wikimedia and the Internet Archive. Ben works globally from Philadelphia. Peter Leonard is an academic librarian with fifteen years’ experience in American research universities, including Columbia, Yale, and Stanford. He founded the Yale Digital Humanities Lab, where he also taught in the department of Statistics and Data Science. He received his PhD in Scandinavian Literature from the University of Washington, and was awarded a Fulbright Fellowship to Uppsala University in support of his doctoral research on Swedish fiction. His postdoctoral work on text-mining Nordic literature was funded by Google. Albin Larsson (User:Abbe98) is a product and software developer with a history of technical contributions to GLAMwiki tooling. |
| Abstract | The explosion of generative AI has brought the digital cultural commons to a precipice, calling us to rethink fundamentals of purpose, participation, sustainability, and power. We face a polycrisis: a weather system of simultaneous, paradoxical, and interacting challenges. It’s a crisis for open access as AI builders aggressively stripmine public data, divert readership, and starve off contributor ecosystems; it’s a crisis of knowledge equity as gaps and biases embed in AI systems at scale; and it’s a crisis of environmental sustainability as hyperscalling of energy-intensive technologies accelerate climate chaos and injustice. But what if these tools could also be used to build a better, more inclusive, more sustainable commons—and, in the process, more ethical and human-aligned AI? This session blends reflections on the current AI moment with practical demonstrations of experimental tools exploring responsible machine learning applications in collection description and discovery. The final portion is hands-on, with opportunities to evaluate tools and seed ideas for continued development and collaboration. |
| Type | Workshop |
| Track | Tools in practice: innovative and good practices, tool innovation, WMF tech infrastructure, metrics, AI. |
| Level | 1 - Everyone can participate in this session |
| Commons File |  |
| Etherpad | Etherpad |
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