Postgraduate Course: Data and AI Ethics, Law and Governance (EFIE11468)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | *Programme Core Course: Data and Artificial Intelligence Ethics (MSc)*
Please Note:
This course is only available to students enrolled on the Data and Artificial Intelligence Ethics (MSc) degree programme.
This course helps students to grasp the relationship between ethics, law, policy and other instruments of governance in the rapidly changing context of AI and other data-intensive technologies. Students learn how ethical and legal norms can steer the production and use of these technologies to align with justice and the reduction of harm, as well as the realisation of benefits for individuals and society. Through collaboration exercises, students practice working with others to govern these technologies wisely and well, while coming to understand today's most significant challenges for effective AI and data governance. |
| Course description |
This course explores the intersection of AI and other data-intensive technologies with law, ethics, policy and other modes of technology governance. Drawing from the disciplines of law, philosophy and political theory, the course will explore how data and AI practice is enabled, constrained, contested and steered by governance frameworks that reflect broad social expectations, individual rights, local and cultural norms, public policy, political and economic incentives.
Students will gain a high-level overview of the most common ethical theories and principles used in the context of AI and data governance, while also learning how data protection and human rights law function as legal approaches to regulating control and power over data, AI and its uses. We will explore how these ethical and legal frameworks, (as well as policy and other governance instruments) shape AI and data-driven research, commerce and innovation, and how they respond (or fail to respond) to wider ethical, social, and political concerns. We will also explore how the design and deployment choices of technologists themselves generate governing power, and the questions this raises about democratic legitimacy and authority.
Throughout the 4-hour teaching blocks, students will work individually, in small groups and as an entire class on complex data-driven innovation case studies that raise a number of legal, ethical and policy issues. Students will learn together to: identify key ethical issues as well as legal concerns; use existing frameworks such as data protection regulation, human rights law, data ethics, algorithmic justice and responsible AI standards to articulate these concerns; weigh and propose potential governance solutions; and identify gaps, value conflicts and political obstacles to effective AI and data governance.
Edinburgh Futures Institute (EFI) - Hybrid Course Delivery Information:
The Edinburgh Futures Institute delivers many of its courses in hybrid mode. This means that you may have some online students joining sessions for this course. 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: in some cases, students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- All presentations, and whole class discussions will be recorded (see the Lecture Recording and Virtual Classroom policies for more details).
You will need access to a personal computing device for this course. Most activities will take place in a web browser, unless otherwise stated. We recommend using a device with a screen, a physical keyboard, and internet access.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2026/27, Not available to visiting students (SS1)
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Quota: 30 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
1) 3,500 Word Essay (100%)
This assessment consists of an individual governance document (report) on a case study.
Learning Outcomes Assessed by Component: 1, 2, 3, 4, 5 |
| Feedback |
Feedback on any formative assessment may be provided in various formats, for example, to include written, oral, video, face-to-face, whole class, or individual. The Course Organiser will decide which format is most appropriate in relation to the nature of the assessment.
Feedback on both formative and summative in-course assessed work will be provided in time to be of use in subsequent assessments within the course.
Feedback on the summative assessment(s) will be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).
Formative Feedback Opportunity:
Formative feedback is ongoing feedback which monitors learning and is intended to improve performance in the same course, in future courses, and also beyond study.
Formative verbal feedback at the group level will be given on the group presentations. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Identify the basic modes of normative governance that co-shape the development and use of technology, and articulate how they embed human values throughout the innovation lifecycle.
- Identify and understand the general shape and function of a range of ethical theories, principles, legal and policy instruments that may be used to govern AI and data-intensive technologies.
- Work with primary and secondary texts in applied ethics, policy and ICT law to discern key governance issues that pertain to a business or research proposal for an AI or data-intensive use case.
- Articulate and critically reflect upon key challenges for, and tensions between, ethical and legal modes of AI and data governance, and associated public concerns about transparency, justice and accountability in technology development and use.
- Apply high-level ethical and legal governance frameworks to an initial assessment of AI and data-driven proposals, for example through a human rights, data protection or ethical impact assessment.
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Reading List
Required Reading:
Boddington, P. (2023). Introduction: Why AI Ethics?. In: AI Ethics. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-19-9382-4_1
Boddington, P. (2020) 'Normative Modes: Codes and Standards.' In The Oxford Handbook of Ethics of AI. Oxford University Press, ed. Dubber, Pasquale and Das. Oxford: Oxford University Press.
Bullock, J.B., et al., eds. The Oxford handbook of AI governance. Oxford University Press, 2024. (selected chapters)
Floridi, L. (2018). 'Soft Ethics, the Governance of the Digital and the General Data Protection Regulation' Philosophical Transactions of the Royal Society A 376: 20180081.
Kazim, E. and A.S. Koshiyama (2021) 'A High-Level Overview of AI Ethics. 'Patterns 2: 1-12. https://doi.org/10.1016/j.patter.2021.100314
Roberts, H., Hine, E., Taddeo, M. and Floridi, F. 2024. Global AI governance: barriers and pathways forward, International Affairs, Volume 100, Issue 3, May 2024, Pages 1275-1286, https://doi.org/10.1093/ia/iiae073
Susskind, J. (2018) Future Politics (New York: OUP), ch. 5
Ulbricht, L & Yeung, K 2021, 'Algorithmic regulation: a maturing concept for investigating regulation of and through algorithms', Regulation & Governance. https://doi.org/10.1111/rego.12437
Veale, M., and F.Z. Borgesius (2021): 'Demystifying the Draft EU Artificial Intelligence Act.' https://arxiv.org/abs/2107.03721
Recommended Reading:
Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press (Chapters 1-2).
Binns, R. 'Data Protection Impact Assessments: A Meta-Regulatory Approach.' International Data Privacy Law 7, no. 1 (2017): 22-35.
Binns, R. and M. Veale (2021) 'Is That Your Final Decision? Multi-Stage Profiling, selective Effects, and Article 22 of the GDPR.' International Data Privacy Law 11(4): 319-332.
Mittelstadt, B. (2017) 'From Individual to Group Privacy in Big Data Analytics.' Philosophy and Technology 30: 475-494.
Carroll, S.R., Garba, I., Figueroa-Rodríguez, O.L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J.D., Anderson, J. and Hudson, M., (2020). 'The CARE Principles for Indigenous Data Governance.' Data Science Journal 19(1):43. http://doi.org/10.5334/dsj-2020-043
Danaher, J. (2016). 'The Threat of Algocracy: Reality, Resistance and Accommodation.' Philosophy and Technology 29: 245-268. https://doi.org/10.1007/s13347-015-0211-1
Edwards, L. and M. Veale (2018) 'Enslaving the Algorithm: From a 'Right to an Explanation' to a 'Right to Better Decisions.'' IEEE Security and Privacy 16(3): 46-54. https://ieeexplore.ieee.org/document/8395080
Floridi, L. (2014) 'Open Data, Data Protection, and Group Privacy.' Philosopy and Technology 27: 1-3.
Friedewald, M., I. Schiering, N. Martin, and D. Hallinan. (2021) 'Data Protection Impact Assessments in Practice.' In European Symposium on Research in Computer Security, pp. 424-443. Springer, Cham.
Hildebrandt, M., and B. Koops. 'The Challenges of Ambient Law and Legal Protection in the Profiling Era.' The Modern Law Review 73, no. 3 (2010): 428-460.
Veale, M., and L. Edwards (2018) 'Clarity, Surprises, and Further Questions in the Article 29 Working Party Draft Guidance on Automated Decision-Making and Profiling.' Computer Law and Security Review 34: 398-404.
Véliz, C. (2019) 'Privacy is Power.' Aeon.
Wachter, S., B. Mittelstadt, and L. Floridi (2017) 'Transparent, Explainable, and Accountable AI for Robotics.' Science Robotics 2(6): 1-6.
Yeung, K. (2016), 'Hypernudge: Big Data As a Mode of Regulation by Design.' Information, Communication and Society, 20(1): pp. 118-136. https://doi.org/10.1080/1369118X.2016.1186713
Yeung, K., A. Howes and G. Pogrebna (2020). 'AI Governance by Human Rights Centred-Design, Deliberation and Oversight: An End to Ethics Washing.' In The Oxford Handbook of Ethics of AI. Oxford University Press, ed.
Dubber, Pasquale and Das. Oxford: Oxford University Press.
Further Reading:
Wright, David and Paul De Hert (eds.), Privacy Impact Assessment, Springer, Dordrecht, 2012 (selections only).
Legislation:
European Commission, Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislativeacts (COM(2021) 206 final)
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), OJ L 119, 27.3.2016, p. 1.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Data,Artificial Intelligence and Ethics,AI,Artificial Intelligence,Data,Ethics |
Contacts
| Course organiser | Mx Claudia Gonzalez-Marquez
Tel:
Email: cgonzal2@ed.ac.uk |
Course secretary | Miss Yasmine Lewis
Tel:
Email: yasmine.lewis@ed.ac.uk |
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