This course focuses on ethics issues in Artificial Intelligence (AI) and Data Science (DS). The main themes are privacy, fairness/bias, and explainability in DS. The objectives are to learn how to identify and measure these aspects in outputs of algorithms, and how to build algorithms that correct for these issues. The course will follow a case-studies based approach, where we will examine these aspects by considering real-world case studies for each of these ethics issues. The concepts will be introduced through a humanities perspective by using case studies with an emphasis on a technical treatment including implementation work. Prerequisite: one of CMPUT 191 or 195, or one of CMPUT 174 or 274 and one of STAT 151, 161, 181, 235, 265, SCI 151, MATH 181, or CMPUT 267.
Section | Capacity | Class times | Login to view Instructor(s) and Location |
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LECTURE A1
(53910) |
180 |
2024-09-03 - 2024-12-09 (TR)
11:00 - 12:20
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Section | Capacity | Class times | Login to view Instructor(s) and Location |
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LAB D01
(53911) |
33 |
2024-09-03 - 2024-12-09 (W)
14:00 - 16:50
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LAB D02
(53912) |
147 |
2024-09-03 - 2024-12-09 (R)
17:00 - 19:50
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Section | Capacity | Class times | Login to view Instructor(s) and Location |
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LECTURE B1
(75703) |
180 |
2025-01-06 - 2025-04-09 (TR)
11:00 - 12:20
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Section | Capacity | Class times | Login to view Instructor(s) and Location |
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LAB H01
(75704) |
155 |
2025-01-06 - 2025-04-09 (T)
17:00 - 19:50
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LAB H02
(77976) |
25 |
2025-01-06 - 2025-04-09 (F)
14:00 - 16:50
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