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CCAI9027 Artificial Intelligence
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Course Description
Artificial intelligence has largely been driven by large technology companies and centralized institutions, shaping how we live, work, and connect. However, recent developments in blockchain and Web3 are challenging this dominance, creating new opportunities for decentralizing AI systems. Examples include AI agents entrusted with making financial transactions, blockchain-based identity systems that distinguish between human and AI users, and decentralized autonomous organizations (DAOs) where humans and AIs interact without traditional hierarchical control.
This course examines these emerging technologies and their potential to transform society by promoting greater transparency, trust, and participation. You will explore questions such as: How might decentralization affect identity, privacy, and security? What are the social and ethical implications of AI systems that operate outside traditional institutions? Through a unique combination of theoretical analysis and practical experimentation with blockchain and AI tools, you will critically assess both the promises and challenges of these technologies.
Topics include the cultural narratives surrounding AI and Web3, the role of digital identities, the impact on human expertise, and the infrastructural integration of blockchain and AI. The course encourages reflection on how decentralized AI could influence various domains like healthcare, education, finance, and governance, shaping social, cultural, and technological futures.
By the end of the course, students will be able to evaluate the societal implications of decentralized AI, understand its ethical considerations, and contribute to discussions on designing fair and accountable digital systems. This course is suitable for those interested in the social dimensions of technology, offering insights into how AI’s decentralization might influence future societal structures.
Course Learning Outcomes
On completing the course, students will be able to:
- Analyze the social implications of decentralised artificial intelligence (Al) and Web3 technologies from a range of perspectives, including sociological and anthropological.
- Evaluate the ethical and societal implications of decentralised AI, including issues of bias, privacy, and security, and design and implement decentralised AI systems that are transparent, accountable, and fair.
- Apply interdisciplinary collaboration to address complex social challenges and promote equity and inclusion in the development and implementation of AI and Web3 technologies.
- Evaluate the potential of decentralised AI and Web3 technologies in various domains, such as healthcare, education, finance, and governance, and speculate on the social, cultural, and technological transformations that may emerge in a decentralised AI ecosystem.
- Develop transdisciplinary expertise across AI and different disciplines, enabling them to reflect on the ways AI is changing society, individuals, and relationships and to adapt to rapid technological advancements in AI while remaining deeply aware of its ethical and societal implications.
Offer Semester and Day of Teaching
First semester (Wed)
Study Load
| Activities | Number of hours |
| Lectures | 24 |
| Tutorials | 10 |
| Reading / Self-study | 36 |
| Assessment: Individual tasks | 36 |
| Assessment: Group project presentation (incl preparation) | 24 |
| Total: | 130 |
Assessment: 100% coursework
| Assessment Tasks | Weighting |
| Creative productions | 25 |
| Mini-project | 25 |
| Group project and presentation | 30 |
| In-class presentation / class discussion | 20 |
Required Reading
Selections from:
- Bess, M. (2015). Our Grandchildren Redesigned: Life in the Bioengineered Society of the Near Future. Beacon Press.
- Boellstorff, T. (2013). Making Big Data, In Theory. First Monday, 18(10).
- Burgess, J. (2023). Everyday data cultures: beyond Big Critique and the technological sublime. AI and Society, 38(3), 1243-1244.
- Campbell-Verduyn, M. (2018). Bitcoin and Beyond: Cryptocurrencies, Blockchains and Global Governance. Routledge.
- Carah, N., Angus, D., & Burgess, J. (2023). Tuning machines: an approach to exploring how Instagram’s machine vision operates on and through digital media’s participatory visual cultures. Cultural Studies, 37(1), 20-45.
- Ensmenger, N. (2010). The Computer Boys Take Over: Computers, Programmers, and the Politics of Technical Expertise. MIT Press.
- Eriksen, T. H. (2016). Overheating: An Anthropology of Accelerated Change. Pluto Press.
- Forsythe, D., & Hess, D. J. (2001). Studying those who study us: an anthropologist in the world of artificial intelligence. Stanford University Press.
- Gershon, I. (2017). Down and Out in the New Economy: How People Find (or Don’t Find) Work Today. University of Chicago Press.
- Helmreich, S. (2008). Silicon Second Nature: Culturing Artificial Life in a Digital World. University of California Press.
- Horst, H., & Miller, D. (2012). Digital Anthropology. Berg Publishers.
- Irani, L. (2019). Chasing Innovation: Making Entrepreneurial Citizens in Modern India. Princeton University Press.
- Kelty, C. M. (2008). Two Bits: The Cultural Significance of Free Software. Duke University Press.
- Malaby, T. M. (2009). Making Virtual Worlds: Linden Lab and Second Life. Cornell University Press.
- O’Dwyer, R. (2023). Tokens: The Future of Money in the Age of the Platform. Verso.
- Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
- Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. Public Affairs.
Course Co-ordinator and Teacher(s)
| Course Co-ordinator | Contact |
| Professor T. McDonald Department of Sociology, Faculty of Social Sciences |
Tel: 3917 1105 Email: mcdonald@hku.hk |
| Teacher(s) | Contact |
| Professor T. McDonald Department of Sociology, Faculty of Social Sciences |
Tel: 3917 1105 Email: mcdonald@hku.hk |