Postgraduate Course: Data and Artificial Intelligence Ethics, Law and Governance (fusion on-site) (EFIE11159)
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 | 10 |
ECTS Credits | 5 |
| Summary | *Programme Core Course: Data and Artificial Intelligence Ethics (MSc/PGD/PGC)*
Please Note:
This course is only available to students enrolled on the Data and Artificial Intelligence Ethics (MSc/PGD/PGC) degree.
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 and group 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, Intellectual Property, 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.
Students will work individually, in small groups and as an entire class on two complex data-driven innovation proposals that raise a number of legal and ethical 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) - 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 |
Course Delivery Information
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| Academic year 2025/26, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 1 |
| Course Start Date |
15/09/2025 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 12,
Other Study Hours 4,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
82 )
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| Additional Information (Learning and Teaching) |
Other Study: Scheduled Group-work Hours (hybrid online/on-campus) - 4
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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) 750 Word Component of Group Governance Document (30%)
The first assessed component (30%) is the group governance assessment document on the second case study. Marks will be based on the clarity, relevance, and coherence of each section, its successful integration with preceding and following material in the group report, as well as the aptness of the interpretation, use and citation of the relevant normative frameworks and supporting literatures.
2) 1250 Word Reflective Analysis (70%)
Individual 1250-word reflection and critical analysis (70%) summarising what students have learned from the group exercise about the challenges of AI and data governance, and drawing from the course readings and lectures to identify further skills or knowledge they would hope to acquire in the MSc programme to enhance their ability to respond to those challenges. Marks will be based on the clarity, focus and level of critical awareness and reflective depth evident. |
| Feedback |
Formative verbal feedback at the group level will be given on the group presentations on the issues and queries, which takes place at the start of Day 2 of the intensive session.
Written summative feedback will be given at the individual level on the 750-word impact assessment and 1250-word critical reflection. |
| 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:
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. |
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.
- Critical knowledge and understanding of the tensions between, and known limitations of, particular approaches, methodologies and interventions.
- 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 produce a coherent normative analysis of risks and concerns in concrete business or other innovation proposals.
- Ability to use a significant range of the principal professional skills, techniques, practices and/or materials associated with the subject/discipline/sector.
- 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
- Management of complex ethical and professional issues and informed judgement on issues not addressed by current professional and/or ethical codes or practices. |
| Keywords | EFI,Level 11,PG,Data,Artificial Intelligence and Ethics,AI,Artificial Intelligence,Data,Ethics |
Contacts
| Course organiser | Prof Shannon Vallor
Tel: (0131 6)50 3886
Email: svallor@ed.ac.uk |
Course secretary | Miss Veronica Silvestre
Tel:
Email: Veronica.Silvestre@ed.ac.uk |
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