Postgraduate Course: Ethics of Robotics and Autonomous Systems (fusion on-site) (EFIE11163)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course introduces students to the new ethical challenges arising from recent advances in artificial intelligence, robotics and autonomous systems. Robots and other autonomous systems are being developed for applications ranging from healthcare and elder care to policing and warfare. In this course we explore how robotic and autonomous technologies can be designed and deployed responsibly and ethically, in ways that enhance rather than degrade human capabilities and wellbeing. |
| Course description |
This course introduces students to the new ethical challenges arising from recent advances in artificial intelligence, robotics and autonomous systems. Robots and other autonomous systems are being developed for applications ranging from healthcare and elder care to policing and warfare. In this course we explore how robotic and autonomous technologies can be designed and deployed responsibly and ethically, in ways that enhance rather than degrade human freedom, opportunity and wellbeing.
Topics can include: the ethical design and use of social robots; the special implications of robots used in care settings; the debate over the moral and legal permissibility of robots in military and policing applications; the impact of robots and autonomous systems on the labour economy and the environment; and the implications of these technologies for human capabilities, rights, virtues and dignity. 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 possibilities of living and working alongside robots and autonomous systems, and how we ought to shape these possibilities to align with just and sustainable futures for humanity.
Edinburgh Futures Institute (EFI) - On-Site 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 be aware that:
- Classrooms used in this course will have additional technology in place: students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- Unless the lecturer or tutor indicates otherwise you should assume the session is being recorded.
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 |
|
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 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 14,
Online Activities 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
82 )
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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 component(s):
1) 1500 Word 'White Paper' / 500 Word Annotated Appendix (100%)
Each student, following the intensive, will choose a case study/controversy in the ethics of robotics and autonomous systems from a prepared list, and conduct independent research on that case/controversy. They will be paired with another student (or two) who have chosen the same study, 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.
Each will 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 for design and regulation (for example, 'should robots ever be allowed to lie to a human?').
Students must also include a 500 word appendix commenting on their discussion with their paired student(s), and what different factual assumptions, perspectives, values and moral reasons the conversation revealed. |
| Feedback |
Formative feedback will be provided in three stages:
- Pre-intensive phase: Dr Richie and Prof Vallor will jointly respond to submitted reading questions from the first week, in a pre-recorded video shared in the second week of the pre-intensive. This will help to shape student understandings of the core issues.
- Intensive phase: In the pre-intensive case studies students will identify salient moral issues and interests in case studies through online posts. Course organisers will provide live formative feedback ofn these posts on Day 1 of the intensive.
- Formative feedback given on Day 2 of the intensive will address the Day 1 collaborative exercises and effective group working dynamics.
- Post-intensive phase: Course organisers and TA will offer written feedback in the second week after the intensives, on an optional 1-2 page outline of the white paper. Outlines must be submitted in a timely manner in order to receive formative feedback.
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, and applications in the field of robot ethics and ethics of autonomous systems, including understandings of the relationships between the concept of a robot, an AI system, an autonomous system, and a machine agent.
- Critically discuss and evaluate a variety of normative perspectives in debates about moral issues in the design, deployment and regulation of robotic and autonomous systems, across multiple domains.
- Work constructively with others to identify salient ethical issues in a case study involving robotic and autonomous systems, form questions that allow deeper investigation, and articulate the relevant moral interests of different groups and stakeholders that developers, regulators and purchasers of these systems must take into account and treat with moral care and respect.
- Produce and clearly communicate for non-specialists in a 'white paper' format a basic analysis and advisory output pertaining to a pressing challenge for ethical design or regulation of robotic/autonomous systems.
- Identify and critically evaluate the different factual assumptions, perspectives, values and moral reasons that shape different positions on key debates in the field of robot/autonomous systems ethics.
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Reading List
REQUIRED READINGS:
WEEK 1: Ethics and politics of data
- Crawford, Kate and Trevor Paglen. 2019. ¿Excavating AI: The Politics of Images in Machine Learning Training Sets.¿ The AI Now Institute https://excavating.ai/
- Vallor, Shannon Vallor. 2018. ¿An Introduction to Data Ethics.¿ Markkula Center for Applied Ethics, pp. 2-39. https://www.scu.edu/media/ethics-center/technology-ethics/IntroToDataEthics.pdf
WEEK 3: Ethics of contemporary data practices
- Robinson, David G. 2022. Voices in the Code. Russell Sage Foundation, Chapters 1-2, 1-54.
- Phan, T., Goldenfein, J., Mann, M., & Kuch, D. (2021). Economies of Virtue: The Circulation of ¿Ethics¿ in Big Tech. Science as Culture, 31(1), 121¿135. https://doi.org/10.1080/09505431.2021.1990875
WEEK 5: Politics and power dynamics in contemporary data practices
- Danaher, John. 2016. ¿The Threat of Algocracy: Reality, Resistance and Accommodation.¿ Philosophy & Technology 29, pp. 245-268. https://doi.org/10.1007/s13347-015-0211-1
- D¿Ignazio, Catherine and Klein, Lauren F. 2020. ¿The Power Chapter.¿ Data Feminism. Cambridge, MA: MIT Press, pp. 21-47.
WEEK 7: Emerging challenges in ethical data practice
- Bender, Emily M., Gebru, Timnit, McMillan-Major, Angelina and Schmitchell, Shmargaret. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? In Conference on Fairness, Accountability, and Transparency (FAccT ¿21), March 3¿10, 2021, Virtual Event, Canada. ACM, New York, NY, USA, 14 pages. https://doi.org/10.1145/3442188.3445922
- Steyerl, Hito. 2023. Mean Images. New Left Review no. 140/141 Mar Jun 2023: 82-97. https://monoskop.org/images/1/19/Steyerl_Hito_2023_Mean_Images.pdf
Week 9: Data ethics as a collaborative social project of futures-building
- Benjamin, Ruha. 2019. Retooling Solidarity, Reimagining Justice. Chapter Five in Race After Technology: Abolitionist Tools for the New Jim Code, Cambridge: Polity Press, pp. 160-197.
- Green, Ben. 2021. Data Science as Political Action: Grounding Data Science in a Politics of Justice. Journal of Social Computing 2(3): 249-265. https://doi.org/10.23919/JSC.2021.0029
SUPPLEMENTAL/ADVANCED READING:
Books
- Amoore, Louise. 2020. Cloud Ethics: Algorithms and the Attributes of Ourselves and Others. Durham: Duke University Press.
- Benjamin, Ruha. 2019. Race After Technology: Abolitionist Tools for the New Jim Code. Cambridge: Polity Press.
- Koopman, Colin. 2019. How We Became Our Data: A Genealogy of the Informational Person. Chicago: University of Chicago Press.
- Vallor, Shannon. 2024. The AI Mirror: How To Reclaim Our Humanity in an Age of Machine Thinking. New York: Oxford University Press.
- Wernimont, Jacqueline. 2019. Numbered Lives: Life and Death in Quantum Media. Cambridge, MA: MIT Press.
Journal Articles and Book Chapters
- Adams Rachel. 2021. Can Artificial Intelligence be Decolonized? Interdisciplinary Science Reviews 46(1-2): 176-197. https://doi.org/10.1080/03080188.2020.1840225
- Charitsis, V., & Lehtiniemi, T. (2023). Data Ableism: Ability Expectations and Marginalization in Automated Societies. Television & New Media, 24(1), 3-18. https://journals.sagepub.com/doi/full/10.1177/15274764221077660
- Fazelpour, Sina, & Danks, David. 2021. Algorithmic Bias: Senses, Sources, Solutions. Philosophy Compass 16( 8), e12760. https://doi.org/10.1111/phc3.12760
- Gabriel, Iason. 2020. Artificial Intelligence, Values, and Alignment. Minds and Machines 30, pp. 411-437. https://doi.org/10.1007/s11023-020-09539-2
- Gal, Uri, Jensen, Tina Blegind, & Stein, Mari-Klara. 2020. Breaking the Vicious Cycle of Algorithmic Management: A Virtue Ethics Approach to People Analytics. Information and Organization 30(2), https://doi.org/10.1016/j.infoandorg.2020.100301
- Hacking, Ian. 2015. Biopower and the Avalanche of Printed Numbers. in Vernon W. Cisney & Nicolae Morar (eds.), Biopower: Foucault and Beyond. Chicago: University of Chicago Press, pp. 65-81.
- Hoffmann, Anna Lauren. 2021. Even When You Are a Solution You Are a Problem: An
Uncomfortable Reflection on Feminist Data Ethics. Global Perspectives 2 (1).
https://doi.org/10.1525/gp.2021.21335
- Kayser-Bril, Nicholas. 2022. Seven Stories from Algorithm Watch. Chapter Four in Currie, Morgan et al. (eds)., Data Justice and the Right to the City. Edinburgh: Edinburgh University Press. pp. 87-115. Open Access https://edinburghuniversitypress.com/pub/media/ebooks/9781474492973.pdf
- Metcalfe, Phillippa. 2022. Hostile Data: Migration and the City: Enacting and Resisting Spaces of Hostility in the UK. Chapter Two in Currie, Morgan et al. (eds)., Data Justice and the Right to the City. Edinburgh: Edinburgh University Press. pp. 46-68. Open Access https://edinburghuniversitypress.com/pub/media/ebooks/9781474492973.pdf
- Mitchell, Shira, Potash, Eric, Barocas, Solon, D¿Amour, Alexander, and Lum, Kristian. 2021. Algorithmic Fairness: Choices, Assumptions and Definitions. Annual Review of Statistics and its Application 8: 141-163. https://doi.org/10.1146/annurev-statistics-042720-125902
- Mohamed, Shakir, Png, Marie-Therese & Isaac, William. 2020. "Decolonial AI: Decolonial Theory and Sociotechnical Foresight in Artificial Intelligence," Philosophy and Technology 33, 659-684. https://doi.org/10.1007/s13347-020-00405-8
- Smith, Anthony KJ, Allegra Schermuly, Christy E. Newman, Lisa Fitzgerald, and Mark DM Davis. Empowering Queer Data Justice. The American Journal of Bioethics 23, no. 11 (2023): 56-58. https://doi.org/10.1080/15265161.2023.2256264 |
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, as related to design, use and regulation.
- 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. |
| Keywords | Robot Ethics,Robotics,Autonomous Agents,Artificial Intelligence,Human Rights,Capabilities,EFI,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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