Welcome and Overview of Trust-A-Thon
Amy McGovern (OU) and David John Gagne (NCAR)

What does it mean to trust? An interdisciplinary perspective
Christopher Wirz (NCAR) and Ann Bostrom (UW)

Insights on (meaningful) interdisciplinary work in the AI/ML development process
Christopher Wirz (NCAR) and Andrea Schumacher (CIRA)

Evaluation metrics for classification and regression
Montgomery Flora (NOAA) and David John Gagne (NCAR)

XAI for traditional machine learning
Montgomery Flora (NOAA) and Amy McGovern (OU)

Explainability vs. Interpretability
Christopher Wirz (NCAR), Ann Bostrom (UW), and Mariana Cains (NCAR)

XAI techniques for deep learning (Part 1)
Amy McGovern (OU), Imme Ebert-Uphoff (CSU), Antonios Mamalakis (CSU), and Randy Chase (OU)

XAI techniques for deep learning (Part 2)
Amy McGovern (OU), Imme Ebert-Uphoff (CSU), Antonios Mamalakis (CSU), and Randy Chase (OU)

Trustworthiness of data and implications for workflows from collection through to designing analyses
Amy McGovern (OU), Douglas Rao (NOAA), Andrea Schumacher (CIRA), and Christopher Wirz (NCAR)

The importance of case studies and tips for using them effectively
Amy McGovern (OU), Douglas Rao (NOAA), Andrea Schumacher (CIRA), and Christopher Wirz (NCAR)

Uncertainty quantification methods (Part 1)
Imme Ebert-Uphoff (CSU), Marie McGraw (CIRA), Ryan Lagerquist (University of Alberta), and Douglas Rao (NOAA)

Uncertainty quantification methods (Part 2)
Imme Ebert-Uphoff (CSU), Marie McGraw (CIRA), Ryan Lagerquist (University of Alberta), and Douglas Rao (NOAA)

Communicating uncertainty: Numerical and verbal representations, visualizations, mapping
Ann Bostrom (UW) and Christopher Wirz (NCAR)