Thursday 2-5 pm
Zoom: https://utoronto.zoom.us/j/81566121258
Description
This course introduces critical social analysis of the ethical aspects of Artificial Intelligence. Students will learn about the theories of ethics, the history of AI, the intersection between ethics and computing, the underlying values of AI, privacy concerns around AI applications, different kinds of biases (based on race, gender, age, sexual orientation, faith, geographic location, etc.) associated with many AI applications, the concerns around AI, and associated debates based on contemporary examples. This course will prepare students for systematically analyzing and auditing an AI system for its ethical standards, and designing new systems that are fairer.
Learning Objectives
- Learn the important theories of intelligent systems and their criticisms.
- Learn the complex relationship between AI and society.
- Learn various methodologies for AI fairness research and their limitations.
- Learn the contemporary ethical challenges that AI researchers are facing today.
- Future directions of “ethical AI”
Teaching Assistant
Evaluation
- Class participation: 10%
- Due: during every lecture.
- The evaluation is based on the student’s active participation in discussion and question-answering, depth of understanding of the subject matter, skills of connecting different theories, the ability of critical analysis, and strength in creative thinking.
- Bi-weekly report: 30%
- Due:
- The first reflection assignment is due on Sept 27th at 11:59 pm.
- The second reflection assignment is due on Oct 10th at 11:59 pm.
- The third reflection assignment is due on Oct 24th at 11:59 pm.
- The fourth reflection assignment is due on Nov 14th at 11:59 pm.
- The fifth reflection assignment is due on Nov 28th at 11:59 pm.
- 1-1.5 page (without reference) analysis of the case study. The analysis needs to be based on the required readings.
- Format: 1-1.5 page PDF. 12pt Times New Roman, single-spaced with 1’’ margins.
- Five assignments in total. Each of 6 points
- If submitted at a later date, 20% will be deducted per day (so, no grade if delayed by 5 days or more).