Undergraduate Course: Algorithms, AI and Society (SCIL10101)
Course Outline
| School | School of Social and Political Science |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course demystifies today's world of artificial intelligence (AI) and algorithms, connecting it to classic and contemporary sociological issues. The twin objectives are to provide distinctively sociological ways of understanding the digital world and to refresh sociological theorising. The course will take a fresh look at classic sociological topics such as power and introduce today's new debates on the role of the 'other-than-human' (such as digital systems) in social life. |
| Course description |
Algorithms, AI and Society will provide an accessible introduction (which does not require any prior technical knowledge) to the technological, social, economic and political forces shaping algorithms and artificial intelligence systems and their uses. The course will demystify important aspects of digital systems (e.g. how large language models such as those underpinning ChatGPT actually work) and discuss the implications for sociology of those systems. Indicative topics include:
the influence of machine-learning algorithms on people's life chances;
the extent of the racialisation and gendering of digital systems;
the scale of the training data and electricity-hungry infrastructure needed by large language models;
the rise of 'algorithmic management' and gig work.
The course consists of ten two-hour sessions (which include both a lecture plus Q&A and a seminar/tutorial around specific topics/questions), along with reading and critically evaluating relevant literature. Learning outcomes include demonstrating knowledge and critical understanding of relevant sociological theories, including the extent to which they are validated or undermined by empirical evidence.
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Information for Visiting Students
| Pre-requisites | Students should have experience of sociology, and must have passed an introductory course in the subject at their home university. |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate knowledge and a critical understanding of sociological theories, concepts and approaches relevant to the study of algorithms, artificial intelligence and their uses.
- Understand and be able to evaluate critically the argument that the role of the 'other-than-human' (such as digital systems) in social life requires us to rethink the conceptual basis of sociology.
- Demonstrate the capacity to assess the strengths and weaknesses of relevant theories, concepts and approaches, including the extent to which they are validated or undermined by empirical evidence.
- Draw critically on a range of resources and materials, not restricted to academic texts, to inform sociological understanding, argument and analysis.
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Reading List
Buolamwini, Joy (2023) Unmasking AI: My Mission to Protect What Is Human in a World of Machines. New York: Random House.
Eubanks, Virginia (2017) Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: St. Martin's Press.
Fourcade, Marion, and Kieran Healy (2024) The Ordinal Society. Cambridge, MA: Harvard University Press.
Hao, Karen (2025) Empire of AI: Inside the Reckless Race for Total Domination. London: Allen Lane.
Latour, Bruno (2005) Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford: Oxford University Press. |
Additional Information
| Graduate Attributes and Skills |
Students on this course will develop and demonstrate:
1. The ability to be independent learners committed to continuous reflection. Sociological knowledge of the topics to be covered (and therefore the literature on them) is emergent rather than settled, and so demands critical evaluation.
2. The ability to sustain intellectual interest by remaining receptive to new ideas and evidence. Students will be encouraged, e.g., to weigh up in the light of empirical evidence common preconceptions about the digital world, 'filter bubbles', etc.
3. The ability to respond effectively to unfamiliar issues in unfamiliar contexts, such as the role of semi-autonomous algorithmic trading systems in financial markets.
4. The ability to make effective use of written and oral means to critique, negotiate, create and communicate understanding.
5. The ability to understand and promote effectively the values of diversity and equity, especially in novel contexts such as the development and use of AI tools such as large language models.
6. The ability to apply understanding of social risks in relation to diverse stakeholders, while simultaneously appreciating the benefits, actual and potential, of today's digital systems. |
| Keywords | Artificial intelligence,technology and society,class,race,gender,actor-network theory |
Contacts
| Course organiser | Prof Donald MacKenzie
Tel: (0131 6)50 3980
Email: D.Mackenzie@ed.ac.uk |
Course secretary | |
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