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CCAI9003 Artificial Intelligence
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Course Description
Artificial Intelligence (AI) systems are rapidly improving in capabilities. This rapid advancement poses a potentially catastrophic risk to humanity. This course is an interdisciplinary introduction to AI safety. Students will learn about (i) present and future capabilities of AI systems; (ii) the potentially catastrophic risk that AI systems pose to humanity; (iii) AI alignment, which seeks to give AI systems goals that reflect human values; (iv) AI governance, which seeks to design optimal regulations and institutions for controlling AI systems; and (v) foundational ethical and philosophical challenges in designing safe AI. Emphasizing an interdisciplinary approach, the course draws on perspectives from computer science, philosophy, political science, and complex systems theory. Through discussions, case studies, and policy briefs, students will critically analyse real-world scenarios where AI safety plays a pivotal role.
Course Learning Outcomes
On completing the course, students will be able to:
- Communicate effectively regarding core catastrophic risks surrounding AI systems.
- Understand core questions related to AI alignment.
- Explain key proposals in AI governance.
- Demonstrate competence in concepts related to safety engineering and complex systems.
Offer Semester and Day of Teaching
First semester (Wed)
Study Load
| Activities | Number of hours |
| Lectures | 24 |
| Tutorials | 12 |
| Reading / Self-study | 60 |
| Assessment: Essay / Report writing | 24 |
| Assessment: Presentation (incl preparation) | 12 |
| Total: | 132 |
Assessment: 100% coursework
| Assessment Tasks | Weighting |
| Case analysis | 20 |
| Issue papers | 20 |
| Design proposal | 20 |
| Reflective journal | 10 |
| Group presentation | 20 |
| Tutorial discussion | 10 |
Required Reading / Viewing
Philosophy of Risk
- Hansson, S.O. (2023). Risk. Stanford Encyclopedia of Philosophy. From https://plato.stanford.edu/entries/risk/#Ethi
Alignment
- Russell, S. (2022). Human-compatible artificial intelligence. In S. Muggleton & N. Chater (Eds.), Human-Like Machine Intelligence 1 (pp. 3-22). Oxford University Press.
Existing Capabilities
- Anthropic. (2024). Alignment faking in large language models.
- Anthropic. (2025). Agentic Misalignment: How LLMs could be insider threats.
- Apollo Research/OpenAI. (2024). Frontier models are capable of in-context scheming.
Beneficial AI and Machine Ethics
- Bykvist, K. (2017). Moral uncertainty. Philosophy Compass, 12(3), e12408.
- Conitzer, V., Freedman, R., Heitzig, J., Holliday, W. H., Jacobs, B. M., Lambert, N., … & Zwicker, W. S. (2024). Social choice should guide ai alignment in dealing with diverse human feedback. arXiv preprint arXiv:2404.10271.
- Propublica. (2016). Machine bias.
Catastrophic Risk
- Bales, A., D’Alessandro, W., & Kirk‐Giannini, C. D. (2024). Artificial intelligence: Arguments for catastrophic risk. Philosophy Compass, 19(2), e12964.
- Cappelen, H., Goldstein, S., & Hawthorne, J. (2026). AI survival stories: A taxonomic analysis of AI existential risk. Philosophy of AI, 1, 1-19.
Governance
- Kak, A., & West, S. M. (2023). (2023). Landscape: Confronting Tech Power: Executive Summary. AI Now Institute. From https://ainowinstitute.org/publications/2023-landscape-executive-summary
Course Co-ordinator and Teacher(s)
| Course Co-ordinator | Contact |
| Professor S.D. Goldstein School of Humanities (Philosophy), Faculty of Arts |
Email: sgold@hku.hk |
| Teacher(s) | Contact |
| Professor S.D. Goldstein School of Humanities (Philosophy), Faculty of Arts |
Email: sgold@hku.hk |