AI, Ethics, and Geoethics (CS 5970)


Module 6: Assignment on Bias, Fairness, and Accountability

Summary

  • (15 min) Read the ethics checklist for data scientists 
  • (5 min) Read the assignment (this one is personalized to your work, it isn’t a case study)
  • (30-45 min) Editing your personalized goals for AI
  • (15-30 min) Bias discussion in your project channel
  • (5 min) grading declaration

Reading

This case study/assignment builds on the chapter you just read in the Getting Started in Data Science book.  The reading is short as I just want you to read over one of the items mentioned in the chapter.  She discusses the “ethics checklist for data scientists” and I would like you to review it. 

Assignment

This one is more specific to you, which is why it is an assignment and not a case study.  I want you to do two things.

  1. Go back to your assignment on creating personal goals for being a responsible user and producer of AI (see the end of Module 4 for details if you missed the original assignment).  Using the ethics checklist for data science, create your own checklist.  As they say in the checklist, it is ok to use their default one and then to edit as needed.  So you do not need to start from scratch!  Start from their list and edit as needed for your research or work.  Keep in mind any of the kinds of bias that are mentioned in the chapter that might turn up in your work. Turn this in on canvas under “Module 6: Updated personal goals for responsible AI”
  2. In your project group, go to your appropriate slack channel, and discuss how each kind of bias may appear in your data.  There are a LOT of kinds of bias and this discussion can become part of your final project product (in a more refined form than a slack conversation).  For now, make sure you discuss this well within your group.

Declarations

  • OU students: After you have done your reading, turned in your assignment, and engaged actively in discussion on your project, complete the grading declaration titled “Module 6: Assignment on Bias, Fairness, and Accountability”