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CCAI9019 Artificial Intelligence
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Course Description
This course introduces key ideas such as AI and Big Data’s impact on economic production, technological progress, data privacy, and AI ethics. The course will offer students an opportunity to reflect on how the rapid technological advancements in AI and Big Data are transforming society, individuals, and relationships. Students will utilize concepts from economics, political science, technology, and ethics to critically analyze the impact of AI and Big Data on society. It encourages students to engage with new political-economic and ethical questions raised by AI and Big Data, promoting interdisciplinary learning and global awareness. The course aligns with the United Nations Sustainable Development Goals of promoting decent work and economic growth and reducing inequalities by examining the impact of AI on the economy and job market.
Course Learning Outcomes
On completing the course, students will be able to:
- Understand, analyze and critically interpret key economic concepts and ideas through applying them to understanding the economics of AI and Big data.
- Apply and integrate knowledge from various disciplines including economics, political science, technology, and ethics to critically assess the transformative impacts of AI and Big Data on society, individuals, and relationships.
- Propose policies to mitigate potential risks and maximize benefits associated with AI and Big Data.
- Demonstrate the communication and collaboration skills on projects related to an issue important to AI & big data.
- Improve ability to communicate effectively and appropriately in diverse cultural contexts.
- Gain experience collaborating remotely within an international team.
Offer Semester and Day of Teaching
First semester (Wed)
Study Load
| Activities | Number of hours |
| Lectures | 20 |
| Tutorials | 8 |
| Seminars | 2 |
| Reading / Self-study | 28 |
| Assessment: Reflective journal | 25 |
| Assessment: Group presentation (incl preparation) | 31 |
| Assessment: In-class peer-reviewed assessment | 6 |
| Total: | 120 |
Assessment: 100% coursework
| Assessment Tasks | Weighting |
| Group presentation | 30 |
| Peer-review of group project presentation | 15 |
| Reflective journal | 45 |
| Class participation | 10 |
Required Reading
- Acemoglu, D., & Johnson, S. (2024). Learning from Ricardo and Thompson: Machinery and labor in the early industrial revolution and in the age of artificial intelligence. Annual Review of Economics, 16(1), 597-621.
- Agrawal, A., Gans, J., & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, 33(2), 31–50. From https://doi.org/10.1257/jep.33.2.31
- Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and prediction: The disruptive economics of artificial intelligence. Harvard Business Press. [Part One]
- Brown, S. (2019, October 31). The lure of ‘so-so technology,’ and how to avoid it. MIT Sloan School of Management. https://mitsloan.mit.edu/ideas-made-to-matter/lure-so-so-technology-and-how-to-avoid-it
- Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. Quarterly Journal of Economics, 140(2), 889–942.
- Carriere-Swallow, M. Y., & Haksar, M. V. (2019). The economics and implications of data: an integrated perspective. International Monetary Fund.
- Dizikes, P. (2024, December 6). Daron Acemoglu: What do we know about the economics of AI? MIT News. From https://news.mit.edu/2024/daron-acemoglu-economics-ai-1206
- Mayer-Schönberger, V. (2025, December). Why data should be shared. Finance & Development. International Monetary Fund. From https://www.imf.org/en/publications/fandd/issues/2025/12/point-of-view-why-data-should-be-shared-viktor-mayer-schonberger
- Millbrook, A. (2023). A short history of tractors in English. The Economist. From https://www.economist.com/christmas-specials/2023/12/20/a-short-history-of-tractors-in-english
- MIT News Office. (2024, December 6). What do we know about the economics of AI? MIT News. From https://news.mit.edu/2024/what-do-we-know-about-economics-ai-1206
- MIT Sloan School of Management. (2019). The lure of “so-so technology” and how to avoid it. Ideas Made to Matter. From https://mitsloan.mit.edu/ideas-made-to-matter/lure-so-so-technology-and-how-to-avoid-it
Course Co-ordinator and Teacher(s)
| Course Co-ordinator | Contact |
| Dr V.W.H. Yuen Faculty of Business and Economics (Economics) |
Tel: 3917 1287 Email: yuenvera@hku.hk |
| Teacher(s) | Contact |
| Dr V.W.H. Yuen Faculty of Business and Economics (Economics) |
Tel: 3917 1287 Email: yuenvera@hku.hk |