27 - 30 Jun 2022
09:00
- 16:00
UTC−06:00
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.
Event Themes
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.
