TAI4ES 2022 Summer School

Trustworthy Artificial Intelligence for Environmental Science

summer school
Jun. 27 to Jun. 30, 2022

9:00 am – 4:00 pm MDT

Virtual

The annual Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School will take place June 27 through June 30. It is organized by the National Science Foundation AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) in conjunction with NCAR.

The TAI4ES Summer School will focus on getting attendees up to speed on how to develop trustworthy AI for the earth and environmental sciences. Participants should have a basic background in AI methods. Each morning, students will participate in lectures from leading researchers in the field. In addition to gaining hands-on experience with evaluating the trustworthiness of AI methods across multiple use cases, participants will gain an understanding of:

  • the foundations of trustworthiness for AI 
  • explanatory AI (XAI) and how explanations, physics, and robustness can help build trust in AI
  • the relationship between ethics and trustworthiness
  • how machine-learning systems have been developed for a range of environmental science applications

Registration Information

Registration is now closed. Email Taysia Peterson with any questions (taysia@ucar.edu). 


Agenda

Links to slides will be added each day. 

Day 1 Slides

Day 2 Slides

Day 3 Slides

Day 4 Slides


Trust-A-Thon

This year’s TAI4ES Summer School will feature a week-long machine learning trust-a-thon. The goal is to evaluate the trustworthiness of pre-trained machine learning algorithms developed to solve real-world environmental science challenges. Participants will develop experience applying a mix of verification, visualization, XAI, and robustness checks to these algorithms to understand how they work and identify biases and failure modes. Both beginner and advanced tracks will be available.


Certificate of Participation

Registered participants can request a certificate of participation by emailing taysia@ucar.edu the week of July 11th. Certificates will not be sent any earlier. 


Program Coordinators

Amy McGovern, University of Oklahoma

David John Gagne, NCAR

Susan Dubbs, University of Oklahoma


Administrator

Taysia Peterson (taysia@ucar.edu)


Code of Conduct

UCAR and NCAR are committed to providing a safe, productive, and welcoming environment for all participants in any conference, workshop, field project or project hosted or managed by UCAR, no matter what role they play or their background. This includes respectful treatment of everyone regardless of gender, gender identity or expression, sexual orientation, disability, physical appearance, age, body size, race, religion, national origin, ethnicity, level of experience, political affiliation, veteran status, pregnancy, genetic information, as well as any other characteristic protected under state or federal law. 
All participants (and guests) are required to abide by this Code of Conduct. This Code of Conduct is adapted from the one adopted by AGU, complies with the directive from the National Science Foundation, and applies to all UCAR-related events, including those sponsored by organizations other than UCAR but held in conjunction with UCAR events, in any location throughout the world. 
Please see our full Code of Conduct.


Partners

logo for NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

logo for NOAA Center for Artificial Intelligence

logo for Learning the Earth with Artificial Intelligence and Physics (LEAP)

Radiant Earth Logo

Google Cloud Platform Logo

​Cloud Compute credits provided by Google Cloud

Contact

Please direct questions/comments about this page to: