一、課程說明 (Course Description)

This AI Ethics, Law, and Society class aims to provide an introduction of
various topics of AI Ethics to help establish a foundation for the students to
better understand the importance, design/principles, technology,
policy/regulation/governance, implementation, and impact of all aspects of AI
ethics. We will discuss these topics from the perspectives of Privacy, Safety,
Fairness, Equality, Robustness, Accountability, Explanability, Human dignity,
Human rights, Democracy, etc. We will also introduce various use cases and
scenarios of challenges in AI ethics (e.g., surveillance, recruitment,
autonomous driving, finance, etc.).

二、指定用書 (Text Books)

Instead of textbooks, there will be pre-reading materials (research papers,
journal articles, blogs, etc.).


三、參考書籍 (References)

Atlas of AI, Kate Crawford, Yale University Press, 2021
The Age of AI: And Our Human Future, Henry A Kissinger, Eric Schmidt, Daniel
Huttenlocher, Little, Brown and Company, 2021
A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives
and How We Can Stay in Control, Kartik Hosanagar, Viking, 2019
Law for Computer Scientists and Other Folk, Mireille Hildebrandt, Oxford
University Press, 2020
Superintelligence, Paths, Dangers, Strategies, Nick Bostrom, Oxford University
Press, 2016
The Oxford Handbook of Ethics of AI, Markus D. Dubber et al., Oxford University
Press, 2021
AI Ethics, Mark Coeckelbergh, The MIT Press, 2020
Tools and Weapons: The Promise and the Peril of the Digital Age, Brad Smith and
Carol Ann Browne, Penguin Books, 2021
Every Leader’s Guide to the Ethics of AI, Thomas H. Davenport and Vivek Katyal,
MIT Sloan Management Review, December 2018
What Do We Do About the Biases in AI?, by James Manyika et al., Harvard Business
Review, October 2019
Putting Responsible AI Into Practice, Rumman Chowdhury et al., MIT Sloan
Management Review, October 2020
The Regulation of AI — Should Organizations Be Worried?, Ayanna Howard, MIT
Sloan Management Review, July 2019
Diversity in AI: The Invisible Men and Women, Ayanna Howard and Charles Isbell,
MIT Management Review, Winter 2021
Research Handbook on the Law of Artificial Intelligence, Woodrow Barfield & Ugo
Pagallo, Elgar, 2018.
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor,
Virginia Eubanks, St. Martin's Press, 2018.
The Cambridge Handbook of the Law of Algorithms, Woodrow Barfield, Cambridge
University Press, 2020.
Artificial Intelligence and Law, Jan De Bruyne & Cedric Vanleenhove,
Intersentia, 2021.
Law and Artificial Intelligence: Regulating AI and Applying AI in Legal
Practice, Bart Custers & Eduard Fosch-Villaronga, T.M.C. Asser Press, 2022
Artificial Intelligence: Law and Regulation, Charles Kerrigan, Elgar, 2022 (in
commercial contexts).
Google AI Principles: https://ai.google/principles/
Microsoft Responsible AI Principles: https://www.microsoft.com/en-
us/ai/responsible-ai?activetab=pivot1%3aprimaryr6

四、教學方式 (Teaching Method)

Lectures (instructors and guest speakers) and interactive discussion (in person
and virtual)


五、教學進度 (Syllabus)

(Week 1) Introduction
(Week 2) AI History and Technology Overview
(Week 3) AI Applications: Scenarios
(Week 4) Guest Speaker #1 (Professor Chao)
(Week 5) AI Design Principles: Fairness and Robustness
(Week 6) AI Design Principles: Privacy and Explainability
(Week 7) Guest Speaker #2 (TBD, Dr. Pin-Yu Chen)
(Week 8) AI Ethics in Law & Society: Discrimination, Bias and Inequality
(Week 9) Global Governance on Facial Recognition Technology and Surveillance:
Features, Driving Forces, and Grand Process
(Week 10) AI Ethics in Business + Term Project introduction (30 minutes)
(Week 11) Legal Regulation of AI Weapons
(Week 12) Unemployment in the AI Age: Legal Responsibility of Government and
Business
(Week 13) Guest Speaker #3 (TBD)
(Week 14) AI Ethics in Law & Society: Autonomous Driving, Smart Healthcare
(Week 15) AI Ethics in Law & Society: Law Enforcement
(Week 16) AI for Good and Social Impacts
(Week 17) Term Project Presentations I
(Week 18) Term Project Presentations II



六、成績考核 (Evaluation)

Quiz 10%
Essays (individual-based) 40%
Term project (team-based) 30%
Class Attendance 10%
Class Participation 10%