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Open educational hands-on tutorial for evaluating fairness in AI classification models #7
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Vision |
Love this! Once you are done, I will share materials with the local university so they can immediately put it to use! |
This awesome! I'm working on AI Ethics and hope we can collaborate on something soon! ❤️ |
Your project is very interesting and really useful! |
Hi @marielaraj, your work looks very interesting! As someone who works with machine learning models, I struggle to eliminate the biases while using the models. It would be really useful to have educational materials dedicated to analyse these biases and help the researchers to create a "fairer" model. Looking forward to your project! 😊 |
Sounds interesting! Best of luck |
Project title: Open educational hands-on tutorial for evaluating fairness in AI classification models
Project Lead: @marielaraj
Mentors: @SLLDeC and @sayalaruano
Timeline
Week 1: Meet your mentor!
If you're a group, each teammate should complete this assessment individually. This is here to help you set your own personal goals during the program. No need to share your results, but be ready to share your thoughts with your mentor.
Before Week 2: Cohort Call (Welcome to Open Life Science!)
Attend call or catch up via YouTube
Create an issue on the OLS-7 GitHub repository for your OLS work and share the link to your mentor.
Draft a brief vision statement using your goals
This lesson from the Open Leadership Training Series (OLTS) might be helpful
Leave a comment on this issue with your draft vision statement & be ready to share this on the call
Check the Syllabus for notes and connection info for all the cohort calls.
Before Week 3: Meet your mentor!
Before Week 4: Cohort Call (Tooling and roadmapping for Open projects)
Week 5 and later
README.md
file, or landing page, for your projectLICENSE.md
CODE_OF_CONDUCT.md
This issue is here to help you keep track of work as you start Open Life Science program. Please refer to the OLS-4 Syllabus for more detailed weekly notes and assignments past week 4.
Week 6
Week 7
Week 8
Week 9
Week 10
Week 11
Week 12
Week 13
Week 14
Week 15
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