This lesson equips participants with trustworthy AI/ML practices, emphasizing fairness, explainability, reproducibility, accountability, and safety across three general data/model modalities: structured data (tabular), natural language processing (NLP), and computer vision. Participants will learn to evaluate and enhance the trustworthiness and reliability of models in each modality. Additionally, they will explore how to integrate these principles into future models, bridging ethical practices with practical applications in their research.

This is a pilot workshop, testing out a lesson that is still under development. The lesson authors would appreciate any feedback you can give them about the lesson content and suggestions for how it could be further improved.

Who: The course is aimed at graduate students and other researchers at UW Madison. Participants must have experience using Python and a basic understanding of machine learning (e.g., familiar with the concepts like train/test split and cross-validation) and should have trained at least one model in the past. Prior experience training neural networks is recommended to get the most out of this workshop.

Where: This training will take place online. The instructors will provide you with the information you will need to connect to this meeting.

When: Dec 2-4, 2024; 9:00 a.m. – 1:00 p.m. Add to your Google Calendar.