AI Utopias and the Climate Crisis

Module overview

Artificial intelligence requires access to data and computation. Both data and computational are material: they are produced by people, made possible by resource extraction, need power to survive, and both inhabit and resculpt the landscape. The use of AI, then, contributes to the climate crisis, but that role can be hard to see, hidden as it often is by a veneer of utopian hype that surrounds the information technology sector. Drawing on scholarship from digital media studies, environmental history, computer science, science and technology studies, climate science, and archival science, this module examines the past, present, and future intersections of AI, data, computation, and the natural environment. It lifts the lid on the countercultural origins of techno-utopianism. It examines the environmental degradation and injustices that techno-utopianism has and continues to hide (e.g. the instrumentalisation of personal climate responsibility). And it opens a pathway for building an intersectional and justice-oriented environmentalist practice in relation to AI.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

You will be able to demonstrate knowledge and understanding of justice-led practices and how to apply those to the production and use of data.
You will be able to demonstrate knowledge and understanding of the role of data in perpetuating injustice.
You will be able to demonstrate knowledge and understanding of effective group work.
You will be able to demonstrate knowledge and understanding of the intersections between data production and it computational uses.
You will be able to demonstrate knowledge and understanding of reflexive data practice.

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

You will be able to apply to your knowledge of what makes for good group work to your wider programme of study.
You will be able to apply to your knowledge of justice-led approaches to data to your wider programme of study.
You will be able to apply to your reflexive data practice to your wider programme of study.
You will be able to apply to your knowledge of how knowledge production, datafication, and algorithmic systems intersect to your wider programme of study.
You will be able to apply to your knowledge of the role of data in perpetuating injustice to your wider programme of study.

Transferable and Generic Skills

Having successfully completed this module you will be able to:

You will be able to act effectively in situations that require group work.
You will be able to act as an informed citizen in your production and reuse of data.
You will be able to act in justice-led ways to the production, use, and reuse of data in wider society.
You will be able to act as an informed citizen in your use of data.
You will be able to act reflexively in your response to injustices amplified by the use of data.

Learning and Teaching

Teaching and learning methods

Teaching methods: - Seminars divided into topic discussion and supervision of group practical work. - Topic mini-lectures will be pre-recorded for use during independent study time. Learning activities include: - In-depth analysis of critical texts - Preparatory reading, practical experimentation, and individual study - Individual participation in seminars and group work

Study time
Type	Hours
Independent Study	114
Teaching	36
Total study time	150

Resources & Reading list

Internet Resources

Memo/Eco-Nft.

Digital Technology and the Planet: Harnessing Computing to Achieve Net Zero.

Journal Articles

Geoffrey Supran and Naomi Oreskes (2021). Rhetoric and Frame Analysis of ExxonMobil’s Climate Change Communications. One Earth, 4(5).

Emily M Bender et al. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?.

Richard Barbrook and Andy Cameron (1996). The Californian Ideology. Science as Culture, 6(1).

Keith Pendergrass et al. (2019). Toward Environmentally Sustainable Digital Preservation. The American Archivist.

Sean Cubitt, Robert Hassan, and Ingrid Volkmer (2011). Does Cloud Computing Have a Silver Lining?. Media, Culture & Society, 33(1).

Textbooks

Kyle Devine (2019). Decomposed: The Political Ecology of Music.

Max Liboiron (2021). Pollution Is Colonialism.

Kate Crawford (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence.

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Project proposal

Assessment Type: Formative
Feedback: Feedback is ongoing and forms part of the teaching as a whole. The students will receive written and verbal feedback on all of their assignments.
Final Assessment: No
Group Work: Yes

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Portfolio	50%
Public outcome	50%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Portfolio	50%
Public outcome	50%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Public outcome	50%
Portfolio	50%

Repeat Information

Repeat type: Internal & External