Explainable & Fair AI Workshop Feedback
The Data Science Hub at UW-Madison is developing a Carpentries workshop on explainable, fair, and responsible machine learning. We want to hear from you about what topics would be valuable to cover.

If you have additional thoughts or feedback, please feel free to reach out to Anna Meyer (apmeyer4@wisc.edu) and Chris Endemann (endemann@wisc.edu).
Sign in to Google to save your progress. Learn more
Name (optional)
Email (optional)
Affiliation (e.g., your department at UW)
What is the standard format / structure of your data? (e.g., tabular data, images, text, etc.)
What models do you typically use in your field? (E.g., computer vision, decision trees, large language models, etc.)
Do you use pre-trained models in your work? If so, what ones?
If you answered yes to the above question, where do you go to find pre-trained models (e.g., HuggingFace, GitHub, PyTorch Hub, etc.)? 
Do you rely on models that have been trained or fine-tuned by other researchers in your field?
Do you or your colleagues ever share or publish the models you train in your work? If so, what mechanism or platform do you use to share your models?
What AI explainability methods do you use to better understand black box models?
If you use explainability methods, what purpose do they serve? (E.g., sanity checks, building stakeholder trust, improving your own understanding of the data, etc.)
What ethical concerns do you have when trying to implement or use AI/ML solutions for your research data? (E.g., data is biased towards certain features or populations, lack of recourse for prediction subjects when the model is deployed, appropriate use of the model, etc.)
Would researchers in your field benefit from a short hands-on workshop focused on a mix of conceptual topics (fairness and bias in machine learning, principles of ethical ML, out-of-distribution concerns, model fallibility and adversarial attacks) and hands-on implementations (AI explainability methods, detecting dataset shift, "confidence intervals" and sensitivity analyses)?
If we have to focus more on either conceptual topics or hands-on implementations due to time constraints, which do you think researchers in your field would benefit from more?
Are there other groups on campus you know of who might be interested in this workshop?
Would you be interested in joining the workshop development team or providing additional advice/input throughout the development process?
Clear selection
Is there anything else you would like to share with the workshop developers (Anna and Chris)?
Submit
Clear form
Never submit passwords through Google Forms.
This form was created inside of UW-Madison Google Workspace.

Does this form look suspicious? Report

Google Forms