Data Science
Fairness, Accountability, Transparency and Ethics (FATE) in Data Science
3 credits
Ethical application of data science and machine learning algorithms. Application of ethical theories in real-world case studies. Data ownership, collection, and validity. Algorithm auditing, fairness and transparency. Reducing unfairness in algorithms. Deployment of predictive models and dissemination of results.
Average
85.2%
Students
152
Sections
3
Grade distribution. <50: 2, 50-54: 0, 55-59: 1, 60-63: 1, 64-67: 1, 68-71: 7, 72-75: 6, 76-79: 14, 80-84: 19, 85-89: 38, 90-100: 63.
Historical Averages
2023–2025
Lowest section avg
81.8%
Highest section avg
87.7%
Terms offered
3
Grade data from ubc-pair-grade-data.
| Section | Activity | Status | Time | Instructor |
|---|---|---|---|---|
| 101 | Lecture | Open | Wed 13:30-15:00, Fri 13:30-15:00 | Giulia Toti |
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