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AI and Society: Critical and Cultural Perspectives

Module description

This module engages with advanced ideas and upcoming trends in the field of non-human intelligence. We will explore a range of perspectives, critiques, and philosophical ideas that can be used to analyse the functionalities, engagement, and creative processes that AI systems (generative and non-generative) offer users. Some of the perspectives the module will engage with include technological determinism, accessibility, digital inequalities, immersive and engagement cultures, hegemonic challenges with AI systems (language, region, class, gender, etc.) and the ethical and environmental costs of current and ongoing developments in the field. The module will combine new ways of thinking about AI technologies, whilst providing you with analytical skills and knowledge that will help you to be both informed about developments in the field and to engage with them critically and meaningfully.

Module aims - intentions of the module

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. Demonstrate a broad and comprehensive knowledge of key developments in the intersection of AI, Communications and Media Studies
• 2. Show an in-depth understanding of AI systems that is not only limited to the engagement and information they offer but that shows an understanding of them as part of media ecologies, doing so by critically examining the labour and energy that goes into their training, maintenance, and continued relevance.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 3. Engage with a range of theoretical approaches and concepts pertaining to the analysis of the role of AI in the field of Communication and Media Studies.
• 4. Extend theoretical understandings of concepts to analyse real-world media contexts and practices.

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 5. Demonstrate advanced research and bibliographic skills, and an advanced and intellectually mature capacity to construct a coherent, substantiated argument

Syllabus plan

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Re-assessment notes

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to submit a further assessment as necessary. If you are successful on referral, your overall module mark will be capped at 50%.

Indicative learning resources - Basic reading

Basic reading:

Adam, Alison. 2006 (1998). Artificial Knowing: Gender and the Thinking Machine. London: Routledge.

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623). https://doi.org/10.1145/3442188.3445922

Beniger, James. 1986. The Control Revolution: Technological and Economic Origins of the Information Society. Cambridge: Harvard University Press.

Bonini, T. and Treré, E., 2024. Algorithms of resistance: The everyday fight against platform power. Mit Press.

Broussard, M., 2018. Artificial unintelligence: How computers misunderstand the world. MIT Press.

Diakopoulos, N. (2019). Automating the News: How Algorithms Are Rewriting the Media (pp. 177-203). Cambridge, MA and London, England: Harvard University Press. https://doi.org/10.4159/9780674239302.

Guzman, A. L., & Lewis, S. C. (2020). Artificial intelligence and communication: A Human–Machine Communication research agenda. New Media & Society, 22(1), 70-86. https://doi.org/10.1177/1461444819858691.

Haugeland, John. 1985. Artificial Intelligence: The Very Idea. Cambridge: MIT Press.

Halpern Orit. 2021. "Planetary Intelligence." The Cultural Life of Machine Learning, (eds.) J. Roberge and M. Castell. 227-256. New York: Palgrave Macmillan.

Mackenzie, Adrian. 2015. "The Production of Prediction: What Does Machine Learning Want?" European Journal of Cultural Studies, 8, no. 4-5: 429-445.

Medrado, A. and Verdegem, P. (2024). Participatory Action Research in Critical Data Studies. Interrogating AI from a South-North Approach. Big Data & Society, Vol 11 (1).

Pasquinelli, M., 2023. The eye of the master: A social history of artificial intelligence. Verso Books.

Ricaurte, P. (2022). Ethics for the Majority World: AI and the Question of Violence at Scale. Media, Culture & Society, 44(4), 726-745.

Russell, S. (2021). Human Compatible: Artificial Intelligence and the Problem of Control. Journal of interdisciplinary studies, Vol.33 (1), p.192-194

Srinivasan, J., 2022. The political lives of information: Information and the production of development in India. MIT Press.

Indicative learning resources - Web based and electronic resources

• ELE – https://ele.exeter.ac.uk/

Key words search

Artificial Intelligence; Communication Studies; Media Theory; Algorithms