Name: Epistemic Infrastructure for Responsible and Reliable AI in Earth and Environmental Science
Start: 2026-07-31T09:00:00-0500
End: 2026-07-31T10:30:00-0500

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.

Epistemic Infrastructure for Responsible and Reliable AI in Earth and Environmental Science

Friday July 31, 2026 9:00am - 10:30am CDT

Brazos

Collaborative Notes

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 who are interested in learning more about 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 aimed at sharing ideas and coming to an understanding of what we need and what to prioritize, not a finished-products showcase.

Audience
Researchers actively developing epistemic infrastructure components or interested in the topic and how we can collaboratively work to build these foundations for AI; data producers; model developers; end users; representatives from NSF NCAR, NOAA, NASA, DOE, and university partners.

Speakers

Monica Morrison

Scientist IV, NSF National Center for Atmospheric Research

Philosopher of science + ethicist working on reliable and responsible data and model use (with a particular focus on AI/ML in ESS), and researching the concept of "fitness-for-purpose". Currently in the Computational Information Systems Lab at NSF NCAR. Co-chair of the CMIP Responsible... Read More →