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)