Postgraduate Course: Environmental Sustainability and Artificial Intelligence (fusion online) (EFIE11291)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Sustainable AI considers the environmental impact of artificial intelligence and other data-intensive technologies and emphasises policies and practices to make AI more sustainable now and in the future. Through in-class exercises, lectures from experts across the disciplines, and innovative course materials, students will engage and learn to shape current debates about AI's potential uses for planetary health versus AI's carbon footprint and links to environmentally and socially unsustainable practices. This course will be of interest to students interested in climate justice, technology, and shaping a more sustainable future. |
| Course description |
Sustainable AI considers the environmental impact of artificial intelligence and other data-intensive technologies and emphasizes policies and practices to make AI more sustainable now and in the future. Many see AI technologies as a key to future discoveries in clean, renewable energy, agricultural resilience, climate forecasting and harm mitigation. Others note that AI's growing carbon footprint and escalating impacts on the supply of water and rare earth minerals are unsustainable, as are many of the social and economic practices associated with the technology.
Through in-class exercises that develop skills of ethical assessment and deliberation, expert lectures from across the relevant disciplines, and contemporary course materials, students will learn to engage and shape these debates, and to lead more effectively with a variety of stakeholders on ethical interventions and policies to align AI with environmental health and sustainability. This course will be of interest to students concerned with climate justice, technology, and shaping a more sustainable future.
Topics focus on the theory and application of environmental ethics in the domain of artificial intelligence and other data-intensive technologies. Along with ethical background and an introduction to sustainability concepts, topics the course covers might include: greenwashing AI; eco-exploitation of AI resources; metrics of sustainability in AI; AI's potential for environmentally beneficial uses, policy and laws for sustainable AI; the carbon cost of 'innovation', climate justice in AI technology; climate fairness in AI use; and data minimization as a conservationist strategy, among others.
Learning in an innovative hybrid and intensive mode that brings together online and in-person students, you will work together in collaborative groups to practice deliberating with others about the environmental impact (both positive and negative) of data-driven technologies and how to align technology with climate ethics to safeguard justice and flourishing for humans and planet alike.
Edinburgh Futures Institute (EFI) - Online Fusion Course Delivery Information:
The Edinburgh Futures Institute will teach this course in a way that enables online and on-campus students to study together. This approach (our 'fusion' teaching model) offers students flexible and inclusive ways to study, and the ability to choose whether to be on-campus or online at the level of the individual course. It also opens up ways for diverse groups of students to study together regardless of geographical location. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities. Students should note that their interactions may be recorded and live-streamed. There will, however, be options to control whether or not your video and audio are enabled.
As part of your course, you will need access to a personal computing device. Unless otherwise stated activities will be web browser based and as a minimum we recommend a device with a physical keyboard and screen that can access the internet.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: 10 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 11,
Online Activities 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
85 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Summative Assessment:
The course will be assessed by means of the following assessment components:
1) Individual 1500-word 'white paper' / 500-word Appendix (100%)
Students will conduct independent research on a chosen case study/controversy/policy challenge in the use of AI or other data-intensive technology as it relates to environmental impact. They will be paired with another student who has chosen the same topic area, and asked to work independently for the first week, but meet in the second to share their answers to key questions in the case study and deliberate about areas of reasoned disagreement as well as consensus.
Students wiill then write an individual 1500 word 'white paper' on their case study for a nonspecialist audience, making a moral argument and advisory recommendation in response to a case study question with practical implications of using AI in a time of climate emergency.
Students must also include a 500 word appendix commenting on their discussion with their paired student, and what different factual assumptions, perspectives, values and moral reasons the conversation revealed to keep them accountable for paired work. |
| Feedback |
Formative feedback will be provided in the immersive phase by the course organiser leading the Q&A session in the second week, who will jointly help to shape student understandings of the core issues and the first collaborative task.
Additional live formative feedback will be given on the group presentations of the case studies during the Day 1 intensive. This feedback will invite a general class discussion of group dynamics and project management to address any potential difficulties groups may encounter.
Written summative feedback will be provided on the individual summative assessments, following the post-intensive application phase. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a basic understanding of key concepts, theories, metrics, and principles of sustainability, AI, ecology, and ethics.
- Critically discuss and evaluate a variety of perspectives in debates on how various AI and data-intensive technologies support or hinder climate justice, fairness, planetary and ecological health, and other global sustainability values.
- Work constructively with others to weigh sustainability ethics, fairness, and justice considerations and identify potential remedies and interventions for sustainable AI and other technologies, by extension.
- Produce and clearly communicate for non-specialists a basic analysis and advisory output pertaining to sustainability ethics, fairness, and justice for sustainable AI and other technologies, by extension.
- Identify and critically evaluate the technical and moral trade-offs involved in decisions about which sustainability frameworks, metrics or interventions to employ in a given AI application context, while weighing these against the broader aims of climate justice and planetary and human flourishing.
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Reading List
Indicative Reading List:
Essential Reading:
Falk, S. and van Wynsberghe, A., 2023. Challenging AI for Sustainability: what ought it mean?. AI and Ethics, pp.1-11.
Sam Gould, 'Green AI: How can AI solve sustainability challenges?,' Deloitte, 04 Jun. 2020, at https://www2.deloitte.com/uk/en/blog/experience-analytics/2020/green-ai-how-can-ai-solve-sustainability-challenges.html.
Becky Kazansky, New report: At the confluence of digital rights & climate justice (The Engine Room: 12 July, 2022), at https://www.theengineroom.org/wp-content/uploads/2022/07/TER-Executive-Sumary04-07-22.pdf.
Kindylidi,I.;Cabral,T.S. Sustainability of AI: The Case of Provision of Information to Consumers. Sustainability 2021, 13, 12064. https://doi.org/10.3390/ su132112064
Kavitha Prasad, 'Achieving a sustainable future for AI,' MIT Review, June 26, 2023, at https://www.technologyreview.com/2023/06/26/1075202/achieving-a-sustainable-future-for-ai/.
Raper, R.; Boeddinghaus, J.; Coeckelbergh, M.; Gross, W.; Campigotto, P.; Lincoln, C.N. Sustainability Budgets: A Practical Management and Governance Method for Achieving Goal 13 of the Sustainable Development Goals for AI Development. Sustainability 2022, 14,4019. https://doi.org10.3390/ su14074019.
Robbins, S.; van Wynsberghe, A. Our New Artificial Intelligence Infrastructure: Becoming Locked into an Unsustainable Future. Sustainability2022,14,4829. https:// doi.org/10.3390/su14084829.
John J Thomas, 'How AI is helping companies meet sustainability goals,' IBM, July 26, 2023, at https://www.ibm.com/blog/how-ai-is-helping-companies-meet-sustainability-goals/.
Van Wynsberghe, Aimee. "Sustainable AI: AI for sustainability and the sustainability of AI." AI and Ethics 1, no. 3 (2021): 213-218.
Recommended Reading:
Bliek, L. A Survey on Sustainable Surrogate-Based Optimisation. Sustainability 2022, 14, 3867. https://doi.org/10.3390/ su14073867
Genovesi, S.; Monig, J.M. Acknowledging Sustainability in the Framework of Ethical Certification for AI. Sustainability 2022, 14, 4157. https://doi.org/10.3390/su14074157
Halsband, A. Sustainable AI and Intergenerational Justice. Sustainability2022,14,3922. https:// doi.org/10.3390/su14073922
Samuel, G.; Lucivero, F.; Somavilla, L. The Environmental Sustainability of Digital Technologies: Stakeholder Practices and Perspectives. Sustainability 2022, 14, 3791. https://doi.org/10.3390/ su14073791
Further Reading:
Bartmann, M. The Ethics of AI-Powered Climate Nudging - How Much AI Should We Use to Save the Planet? Sustainability2022,14,5153. https://doi.org/10.3390/su14095153
Bolte, L.; Vandemeulebroucke, T.; van Wynsberghe, A. From an Ethics of Carefulness to an Ethics of Desirability: Going Beyond Current Ethics Approaches to Sustainable AI. Sustainability2022,14,4472. https:// doi.org/10.3390/su14084472
Heilinger, J.C., Kempt, H. and Nagel, S., 2023. Beware of sustainable AI! Uses and abuses of a worthy goal. AI and Ethics, pp.1-12.
Ligozat, A.-L.; Lefevre, J.; Bugeau, A.; Combaz, J. Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions. Sustainability2022,14,5172. https:// doi.org/10.3390/su14095172
Temple, James. 'Bill Gates's energy venture fund is expanding into climate adaptation and later-stage investments,- MIT Review, October 19, 2022, at https://www.technologyreview.com/2022/10/19/1061958/bill-gates-energy-venture-fund-is-expanding-into-climate-adaptation-and-later-stage-investments/ |
Additional Information
| Graduate Attributes and Skills |
Knowledge and Understanding:
- A critical understanding of a range of specialised theories, concepts and principles drawn from multiple disciplinary and practitioner perspectives.
- A critical awareness of current challenges and debates in an emerging research area involving multiple specialisms.
Applied Knowledge, Skills and Understanding:
- Ability to apply critical knowledge to concrete case studies, research outputs, applications and proposals.
- Ability to identify potential challenges in a case study or research output, as related to both design and use contexts.
- Ability to demonstrate originality and/or creativity, including in practice.
Generic Cognitive Skills:
- Development of original and creative responses to problems and issues.
- Capacity to critically review, consolidate and extend knowledge, skills, practices and thinking across disciplines, subjects, and sectors.
- Ability to deal with complex issues and make informed judgements in situations in the absence of complete or consistent data/information.
Communication, ICT, and Numeracy Skills:
- Communication, using appropriate methods, to a range of audiences with different levels of knowledge/expertise.
- Ability to articulate clear and well-justified advisory recommendations.
Autonomy, Accountability, and Working with Others:
- Skills to manage their own individual contribution to a group presentation or report
- The ability to engage constructively and productively in critical debate
- Management of complex ethical and professional issues and informed judgement on issues not addressed by current professional and/or ethical codes or practices. |
| Keywords | Climate Ethics,AI,Sustainability,Environmental Ethics,Data-Driven Technologies,EFI,Level 11,PG |
Contacts
| Course organiser | Dr Cristina Richie
Tel:
Email: crichie2@ed.ac.uk |
Course secretary | Miss Veronica Silvestre
Tel:
Email: Veronica.Silvestre@ed.ac.uk |
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