Replication Project Part 0: Introduction
Class Coverage
- Lecture: How do we run a data science project? What skills are necessary on AI teams? How can/do data scientists work effectively with other roles?
- What does the impossibility theorem mean for organizations employing AI?
- Should we just avoid using AI completely?
- What regulatory efforts help ascertain the definitions of fairness applicable to the use case?
Pre-Class Readings
Please read the following:
- AI fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias (Bellamy et al.)
- Datasheets for Datasets (Gebru et al.)
- AI Fairness 360 Demo
This Week’s Slides
Assigned for Week 04
- Complete next week’s readings
- If you signed up to present on “Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?” (Holstein et al.) come prepared to present.
- Complete default participation questions (activity or readings - up to you)
- Complete part01 of the Replication Project and send notebook to mentors by the 22nd.
- Reach out if you have any issues.
Note: These tasks will be difficult, and will potentially take a while to compute, so start early!