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- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Ichikawa Tagui Suzuki Kenji Sakuma Jun Ono Isao Miyake Yoshihiro
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Wed9-10()
- Group
- -
- Course number
- DSA.A603
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 4Q
- Syllabus updated
- 2024/1/17
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
The rapid evolution of AI improves the convenience of our lives, but it also has social impacts. In this lecture, we teach the ethics of AI in the information society, the information law, and the technologies to realize responsible AI to cultivate a broad perspective beyond the boundaries of the humanities and sciences. This lecture deals with social issues of AI, which were not covered in the course of fundamentals of progressive artificial intelligence.
Student learning outcomes
The goals are to think independently about ethical, legal, and social issues in today's information society and understand the techniques of explainable AI and fairness.
Keywords
AI ethics, Governance, Privacy, Security, Explainable AI, Fairness, Generative AI
Competencies that will be developed
| ✔ Specialist skills | ✔ Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
Class flow
ZOOM is used to allow students to take courses at Ookayama or Suzukakedai campuses.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Social Risks and Ethics of AI | Learn social risks caused by use of AI. |
| Class 2 | Data Security and Safety of AI | Learn data security and concept of safety of AI systems. |
| Class 3 | Privacy and Data Protection in the era of AI | Learn privacy issues including that caused by AI. |
| Class 4 | World Trend of AI Regulation and Governance | Discuss on future of AI regulation and governance. |
| Class 5 | Explainable AI | Learn how to interpret black box models. |
| Class 6 | Fairness in machine learning | Learn data bias and mitigation methods. |
| Class 7 | Ethical, legal, and social issues of generative AI | Consider various issues in development and use of generative AI. |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None.
Reference books, course materials, etc.
Distributed electronically at T2SCHOLA.
Assessment criteria and methods
Evaluation is based on in-class assignments and reports, and advanced assignment reports.
Related courses
- Fundamentals of progressive data science（XCO.T677）
- Exercises in fundamentals of progressive data science（XCO.T678）
- Fundamentals of progressive artificial intelligence（XCO.T679）
- Exercises in fundamentals of progressive artificial intelligence（XCO.T680）
Prerequisites (i.e., required knowledge, skills, courses, etc.)
・Aspiration to increase interest in social issues to learn about ethical aspects of AI.
・Students should have a basic knowledge of machine learning to master responsible AI technologies.
