We are a home for Earth science data and computing professionals. Our sessions bring together the community for hands-on, interdisciplinary deep dives as we explore "Bridging Divides: Data, Technology, Community" this year. Learn more about this theme on the ESIP Meetings page.
Session and plenary recordings will be published on the ESIP YouTube Channel.
As AI and machine learning are increasingly deployed across the Earth and environmental sciences pipeline — from data ingestion through model training, evaluation, and downstream applications — the field faces a shared challenge: how do we know when AI outputs are fit for the purposes to which they are being put? This requires shared epistemic infrastructure: frameworks, vocabularies, documentation standards, readiness assessments, and governance practices that make AI systems legible, evaluable, and accountable. This session brings together researchers developing epistemic infrastructure components including data readiness frameworks, model readiness standards, AI/ML documentation protocols, and pipeline risk assessment approaches. Designed as a structured working dialogue, not a finished-products showcase.
Audience Researchers actively developing epistemic infrastructure components; data producers; model developers; end users; representatives from NSF NCAR, NOAA, NASA, DOE, and university partners
