Open educational hands-on tutorial for evaluating fairness in AI classification models

[marielaraj]

Project title: Open educational hands-on tutorial for evaluating fairness in AI classification models

Project Lead: @marielaraj

Mentors: @slldec and @sayalaruano

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Timeline

Week 1: Meet your mentor!

• [x] Meet mentor for 30 minutes
• [x] Create an account on GitHub
• [x] Check if you have access to the HackMD notes set up for your meetings with your mentor
• [x] Prepare to meet your mentor(s) by completing a short homework provided in your shared notes
• [x] Complete your own copy of the open leadership self-assessment and share it to 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.
• [x] Make sure you know when and how you'll be meeting with your mentor.

Before Week 2: Cohort Call (Welcome to Open Life Science!)

• [x] Attend call or catch up via YouTube

• [x] Create an issue on the OLS-7 GitHub repository for your OLS work and share the link to your mentor.

• [x] Draft a brief vision statement using your goals

  This lesson from the Open Leadership Training Series (OLTS) might be helpful

• [x] Leave a comment on this issue with your draft vision statement & be ready to share this on the call

• [x] Check the Syllabus for notes and connection info for all the cohort calls.

Before Week 3: Meet your mentor!

• [x] Meet mentor
• [x] Look up two other projects and comment on their issues with feedback on their vision statement
• [ ] Complete this compare and contrast assignment about current and desired community interactions and value exchanges
• [x] Complete your Open Canvas (instructions, canvas)
• [x] Share a link to your Open Canvas in your GitHub issue
• [ ] Start your Roadmap
• [ ] Comment on your issue with your draft Roadmap
• [x] Suggest a cohort name at the bottom of the shared notes and vote on your favorite with a +1

Before Week 4: Cohort Call (Tooling and roadmapping for Open projects)

• [x] Attend call or catch up via YouTube
• [ ] Look up two other projects and comment on their issues with feedback on their open canvas.

Week 5 and later

• [x] Meet mentor
• [x] Create a GitHub repository for your project
• [ ] Add the link to your repository in your issue
• [x] Use your canvas to start writing a README.md file, or landing page, for your project
• [ ] Link to your README in a comment on this issue
• [ ] Add an open license to your repository as a file called LICENSE.md
• [x] Add a Code of Conduct to your repository as a file called CODE_OF_CONDUCT.md
• [ ] Invite new contributors to into your work!

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

• [ ] Attend call or catch up via YouTube

Week 7

• [ ] Meet mentor

Week 8

• [ ] Attend call or catch up via YouTube

Week 9

• [ ] Meet mentor

Week 10

• [ ] Attend call or catch up via YouTube

Week 11

• [ ] Meet mentor

Week 12

• [ ] Attend call or catch up via YouTube

Week 13

• [ ] Meet mentor

Week 14

• [ ] Attend call or catch up via YouTube

Week 15

• [ ] Meet mentor

[marielaraj]

Vision We are developing open educational materials that focus on understanding how to study the bias of machine learning models so that educators and AI practitioners can use and adapt the concrete and diverse examples proposed in the materials for their fairness classes.

[diana-pilvar]

Love this! Once you are done, I will share materials with the local university so they can immediately put it to use!

[gigikenneth]

This awesome! I'm working on AI Ethics and hope we can collaborate on something soon! ❤️

[JFormoso]

Your project is very interesting and really useful!

[booktrackerGirl]

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! 😊

[Bsolodzi]

Sounds interesting! Best of luck

[marielaraj]

Hi, everone! You can find the open canvas of this project in this link. You are able to comment in the file if uoy have any recommendation.

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¡Hola! Pueden encontrar el canvas abierto de este proyecto en este enlace. Pueden comentar en el archivo si tienes alguna recomendación.