Скачать или смотреть AI and Natural History Collections: Promise, Pitfalls, and Perspectives | Webinar (Dec 2, 2025)

Artificial intelligence (AI) offers transformative potential for digitizing natural history collections, accelerating the transcription and interpretation of specimen data at scales previously unattainable. Automated image processing, handwriting recognition, and machine learning models can rapidly extract specimen label data, geospatial coordinates, taxonomic information, and even morphometric measurements from images, making collections more accessible to researchers, educators, and the public. However, these advances come with important considerations. Data quality and accuracy must be ensured, as AI-generated outputs can propagate errors if not carefully validated. Ethical issues arise around data sharing, particularly for sensitive locality information that could increase the risk of exploitation of rare or endangered species. Additionally, AI-driven digitization may require substantial infrastructure, investment, and technical expertise, potentially widening the gap between well-funded and under-resourced institutions. This moderated panel discussion will bring together representatives from several institutions to explore the opportunities and challenges of applying AI in natural history collections.
Speakers:
Laura Briscoe (New York Botanical Garden)
Leanna Feder (New York Botanical Garden)
James Mickley (Oregon State University)
Emily Sessa (New York Botanical Garden)
Jordan Teisher (Missouri Botanical Garden)
Kim Watson (New York Botanical Garden)
Will Weaver (University of Michigan)