Replication Project Part 03: Bias Mitigation

Class Coverage

• Mitigating fairness issues in AI through pre-, in-, and post-processing
• Re-learning and re-deploying models.

Pre-Class Readings

Please read the following:

• Model Cards for Model Reporting (Mitchell et al.)
• Fairness Through Awareness (Dwork et al.)
• Demo: Explaining model behavior using LIME

Participation Questions

Submit to Gradescope by 10AM PT on , Thursday November 10th

• In what situations do you think “fairness through awareness” is most appropriate? Are there any situations where “fairness through unawareness” is necessary instead?
• Describe a situation in which a pre-, in-, or post-processing technique might be appropriate for mitigating model bias, and explain what technique you would use.

This Week’s Slides

• Week 07: Replication Project Part 3, Bias Mitigation

Assigned for Week 08

• Complete Replication Project Part 03a
• Complete Replication Project Part 03b - Write up Instructions
• Complete next week’s readings
  • If you signed up to present on Algorithmic Fairness and Vertical Equity (Black et al.) come prepared to present next week and submit your presentation on Gradescope by 10AM PT on Friday, November 18th
• Submit your answers to next week’s participation questions to Gradescope by 10AM PT on Thursday, November 17th

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