Postgraduate Course: Data Cultures: From Critical Theory to Digital Practice (ENLI11258)
|School||School of Literatures, Languages and Cultures
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||This course considers the implications of living in an age in which algorithmic processes, big data and forms of artificial intelligence play an increasingly influential role in social interaction and cultural production. If these algorithms and the big datasets on which they depend are not generally available for public scrutiny, and developments in machine learning are unfolding faster than processes of democratic oversight can keep up with, insights from critical theory can be brought to bear on these technologies to better understand the implications for human subjectivity, autonomy, creativity, privacy, ethical principles and governance. By historicising the always ambivalent relations between human beings and technology, and giving students the opportunity to gather data and carry out analysis of it for themselves, the course aims to help students better grasp the significance of the ways in which data-driven technologies are, sometimes invisibly, changing the ways in which the world is mediated to us and how social relationships are being altered, and to consider how such changes may be registering in contemporary textual and cultural artefacts.
This course seeks to bring together relevant critical theory in the humanities with practical knowledge of how these technologies work 'under the hood', in order to empower students to join in conversations around how technology is, and should be, shaping the human world. It therefore integrates seminar-style discussion of primary and secondary texts with practical instruction on using technologies, both applications with graphical user interfaces and introductory programming for text analysis. No prior knowledge of programming is assumed: the course is not intended to teach programming, but rather to give students a level of computational literacy such that they can understand the basics of what an application or a program is doing, how to find technical help for themselves and, most important, to be able to critically assess what the technology is able to do. Technical tasks will be carefully scaffolded in class, and students need only to be equipped with a willingness to search for answers for themselves and consolidate their learning outside of class. Students who do have some background in informatics or data science are more than welcome on the course: they will have opportunities to extend themselves and to do more complex coding tasks to apply their skills, and will likely find that the challenge (and main source of enjoyment) of the course lies in integrating the technical components of the course with the theoretical material.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- understand some of the main debates around developments in digital technology and machine learning as they apply to questions of ethics, privacy, autonomy, bias, governance and related areas
- understand and apply insights from critical theorists from humanities fields to these socio-technical developments
- use software and/or programming languages to gather data and perform computational analysis for themselves
- reflect on the implications of their theoretical and practical knowledge for their own status as data subjects
Primary texts will range across genres (television shows, films, short stories, non-fiction essays, poems) and will change over time to reflect the contemporary focus of the course. A representative sample might include the following:
Selected episodes from Charlie Brooker et al, Black Mirror. Netflix, 2011-19.
Jonze, Spike et al. Her. 2014.
Selections from Jia Tolentino, Trick Mirror: Reflections on Self-Delusion. 4th Estate, 2019.
Selections from William Carlos Williams, the Futurist Manifesto, Carlyle's 'Signs of the Times', to be provided on Learn.
There are fewer primary texts than is habitual for courses on the MSc Literature and Modernity, as a substantial part of the course will be devoted to teaching students the programming language(s) and specialised software that they will need to work with data themselves.
Acker, Amelia, and Joan Donovan. 'Data Craft: A Theory/Methods Package for Critical Internet Studies'. Information, Communication & Society, vol. 22, no. 11, Sept. 2019, pp. 1590-609.
Anderson, Michael, and Susan Leigh Anderson, editors. Machine Ethics. Cambridge University Press, 2011.
Awret, Uziel, editor. The Singularity: Could Artificial Intelligence Really Out-Think Us (And Would We Want It To?). Imprint Academic, 2016.
Bostrom, Nick. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2016.
boyd, danah. 'You Think You Want Media Literacy ... Do You?' Data & Society: Points, 9 Mar. 2018.
boyd, danah, and Kate Crawford. 'Critical Questions for Big Data'. Information, Communication & Society, vol. 15, no. 5, June 2012, pp. 662-79.
Breiman, Leo. 'Statistical Modeling: The Two Cultures'. Statistical Science, vol. 16, no. 3, Aug. 2001, pp. 199-231.
Butler, Judith. 'Performative Acts and Gender Constitution: An Essay in Phenomenology and Feminist Theory'. Theatre Journal, vol. 40, no. 4, 1988, pp. 519-31.
Clement, Tanya, and Amelia Acker, eds. Special issue on Data Cultures, Culture as Data. Journal of Cultural Analytics, Apr. 2019.
Criado-Perez, Caroline. Invisible Women: Exposing Data Bias in a World Designed for Men. Chatto & Windus, 2019.
Debord, Guy. Society of the Spectacle. 1967. Translated by Ken Knabb, Rebel Press, 1992.
Feng, Alice, and Shuyan Wu. 'The Myth of the Impartial Machine'. Parametric Press, no. 1, Spring 2019.
Goffman, Erving. The Presentation of Self in Everyday Life. 1959. Penguin, 1990.
Graham, Elyse. The Republic of Games: Textual Culture Between Old Books and New Media. McGill-Queen's University Press, 2018.
Hayles, Katherine. How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. The University of Chicago Press, 1999.
Hill, Kashmir. 'Life Without the Tech Giants'. Gizmodo, Jan. 2019.
Irving, Geoffrey, and Amanda Askell. 'AI Safety Needs Social Scientists'. Distill, vol. 4, no. 2, Feb. 2019, p. e14.
Jones, Matthew L. 'How We Became Instrumentalists (Again): Data Positivism since World War II'. Historical Studies in the Natural Sciences, vol. 48, no. 5, Nov. 2018, pp. 673-84.
Lin, Patrick, and Keith Abney, editors. Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence. Oxford University Press, 2017.
Mackenzie, Adrian. Machine Learners: Archaeology of a Data Practice. MIT Press, 2017.
Morrison, Aimee. 'Facebook and Coaxed Affordances'. Identity Technologies: Constructing the Self Online, edited by Anna Poletti and Julie Rak, University of Wisconsin Press, 2014, pp. 112-31.
Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press, 2018.
Odell, Jenny. How to Do Nothing: Resisting the Attention Economy. Melville House, 2019.
O'Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Penguin Books, 2017.
Rahwan, Iyad, et al. 'Machine Behaviour'. Nature, vol. 568, no. 7753, Apr. 2019, pp. 477-86.
Shane, Janelle. Excerpt from 'Unfortunate Shortcuts'. You Look Like a Thing and I Love You. Wildfire, 2019, pp. 173-184.
Underwood, Ted. 'Why an Age of Machine Learning Needs the Humanities'. Public Books, 5 Dec. 2018.
Vaneigem, Raoul. The Revolution of Everyday Life. Translated by Donald Nicholson-Smith, Rebel Press, 1983.
Wallach, Wendell, and Colin Allen. Moral Machines: Teaching Robots Right from Wrong. Oxford University Press, 2009.
Young, Damon R. 'Ironies of Web 2.0'. Post45, no. 2, May 2019.
Zuboff, Shoshana. The Age of Surveillance Capitalism: The Fight for the Future at the New Frontier of Power. Profile Books, 2019.
|Graduate Attributes and Skills
||From the Graduate Attributes framework:
Curiosity for learning that makes a positive difference: Students will be shown how to find relevant scholarship and shown how to use software to share this with their peers and create a learning resource for the whole group.
Courage to expand fulfil their potential: Students will be challenged to go beyond their disciplinary training and bring together theory and practice from a range of fields.
Creative problem solvers and researchers: Students will be asked to use computational methods to address questions and issues of interest to humanities researchers.
Critical and reflective thinkers: Students will synthesise a variety of theoretical approaches from the twentieth and twenty-first centuries and apply them to technological developments in the contemporary world.
Skilled communicators: Students will demonstrate their ability to communicate complex ideas in spoken and written form.
Students will also gain a knowledge of software applications and a basic grasp of programming concepts.
|Keywords||data science,critical theory,digital humanities,machine learning,natural language processing
|Course organiser||Dr Anouk Lang
Tel: (0131 6) 5 50 8936
|Course secretary||Miss Kara McCormack
Tel: (0131 6)50 3030