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 CMPUT 195, or one of CMPUT 174 or CMPUT 274 and one of STAT 141, STAT 151, STAT 235, STAT 265, SCI 151, MATH 181, or CMPUT 267, or consent of the instructor.
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
LECTURE B1
(16952) |
90 |
2024-01-08 - 2024-04-12 (TR)
11:00 - 12:20
SAB 3-36
|
Primary Instructor: Nidhi Hegde
|
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
LAB H01
(16953) |
90 |
2024-01-08 - 2024-04-12 (F)
14:00 - 16:50
SAB 3-36
|
|
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 | Instructor(s) |
|---|---|---|---|
|
LECTURE A1
(53910) |
180 |
2024-09-03 - 2024-12-09 (TR)
11:00 - 12:20
ETLC E1-017
|
|
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
LAB D01
(53911) |
32 |
2024-09-03 - 2024-12-09 (W)
14:00 - 16:50
CSC 1-59
|
|
|
LAB D02
(53912) |
148 |
2024-09-03 - 2024-12-09 (R)
17:00 - 19:50
CCIS 1-140
|
|
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 | Instructor(s) |
|---|---|---|---|
|
LECTURE B1
(75703) |
180 |
2025-01-06 - 2025-04-09 (TR)
11:00 - 12:20
T LB-002
|
|
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
LAB H01
(75704) |
156 |
2025-01-06 - 2025-04-09 (T)
17:00 - 19:50
CCIS L1-140
|
|
|
LAB H02
(77976) |
24 |
2025-01-06 - 2025-04-09 (F)
14:00 - 16:50
CSC 1-25
|
|