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Critical AI Studies

Module description

This module explores the recent rise of ‘artificial intelligence’ from the interdisciplinary perspectives afforded by Comparative Studies. Comparative Studies is ideally situated to study algorithms, not as mathematical abstractions, but as material practices predating human tools and modern machines. Specifically, we will look to the emergence of the concept of artificial intelligence as it emerged from wide-ranging algorithmic practices from the mid-20th century to the present. We will consider the subsequent rise of automation in fields as diverse as financial analysis, climate modeling, social media, crime prediction, and even artistic practice. As such, this module will consider how automation affects culture, art, and politics in the present. We will study the algorithmic cultures that lead to recent computational breakthroughs asking – what exactly is a science of artificiality? As such, our approach will be both historical and theoretical as we trace the culture of artificial intelligence. We will draw from the history of science, cultural studies, intermedial art forms, films, and literature, as we critique the rise of big data in everyday life. There are no pre-requisites or co-requisites for this module, and no specialist knowledge, skills, or experience are required to take it.

Module aims - intentions of the module

This module aims to:

• Provide you with a comprehensive understanding of the foundational cultural concepts and techniques that legitimate the automation of knowledge production, from the early beginnings of AI, through to its present ubiquity and the risks and possibilities it poses for the future.
• Enable students to investigate the transformation that is ongoing throughout society through an analysis of both the technical algorithms in specific AI applications as well as the wider public discourse circulation around said applications.
• Equip students with the conceptual toolkit to form their own positions on debates around AI today within politics, culture, the arts, and in the media and communications landscape.
• Assist in the application of interdisciplinary perspectives to assess and act on the truth claims made through automated knowledges across fields.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

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

• 1. Demonstrate a clear understanding of the foundational technologies and ideas that make up AI
• 2. Analyse the historical developments of cultural techniques that have led to the ubiquity of AI applications today
• 3. Critically evaluate the influence of economic development on automation and the cultural politics that have emerged through a shift in downstream media and communications technologies like the internet, social media, streaming services etc.

ILO: Discipline-specific skills

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

• 4. Apply key theories and concepts from media and communication studies to evaluate the evolution of AI and its implications for culture.
• 5. Contextualise debates around AI use from human rights to labour disputes, to the culture industry.

ILO: Personal and key skills

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

• 6. Manage relevant learning resources, learning strategies and your own time confidently and independently.
• 7. Demonstrate advanced research and bibliographic skills

Syllabus plan

The content will vary from year to year, but it is envisioned that the module will cover some or all of the following topics:

• Introduction to Artificial Intelligence
• Algorithmic Culture
• Data Dispossession
• Automation and Labor
• Artificial Selves
• Race, Gender, Sexuality, Ability, and Surveillance
• Learning, Remembering, Generating
• Brains, Eyes, Decisions
• Prediction and Fears of the Future
• Digital Humans
• Artificial Art

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 40%) 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 40%.

Indicative learning resources - Basic reading

• Nakamura, Lisa. "Indigenous Circuits: Navajo Women and the Racialization of Early Electronic Manufacture." 2014.
• O'Neil, Cathy. "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy." 2017.
• "How Reason Almost Lost Its Mind: The Strange Career of Cold War Rationality" 2013.
• Ferreira da Silva, Denise . “Accumulation, Dispossession & Debt: The Racial Logic of Global Capitalism." 2012.
• Browne, Simone. "Dark Matters: On the Surveillance of Blackness." 2015.
• Mackenzie, Adrian. "Machine Learners: Archaeology of a Data Practice." 2017.
• G Griziotti. "Neurocapitalism: Technological Mediation and Vanishing Lines." 2019.
• "The People's Guide to A.I." Pioneer Works 2019

Key words search

Internet, Web, AI, Social media, News, Algorithms, Platforms