Postgraduate Course: Data and AI Ethics as a Practice (EFIE11474)
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.
What are the challenges and frictions entailed in applying data and AI ethics - a body of techniques for living well together with these new technologies - in practical domains, such as those of healthcare, engineering, and criminal justice? How can we study ethical, responsible and sustainable research and uses of data and AI in applied settings and workplaces? How do we understand and bridge the communications gaps between data ethicists and computing professionals in terms of values, assumptions and practices?
In this course, we use case studies and exercises to explore the challenges of ethical data practice and provide you with methodologies to work with others to meet these challenges in academic, private and public sectors. |
| Course description |
This course looks at the role of data and AI, as well as its practitioners themselves, in practical ethics. In this frame, ethics and data practices are fundamentally intertwined, as data practices always embed values into the design of our future worlds. What questions must practitioners ask to decide how to enact ethics, or acknowledge trade-offs? What role do institutions, laws, and the broader political economy play in how individuals carry out practical ethics in specific contexts?
We consider the relationship of data and AI to ethics in the practical domains of engineering, criminal justice and healthcare, among others. We examine how ethical values and duties of care (justice, fairness, privacy, safety, integrity, accountability, humility and respect for human dignity) can guide responsible and sustainable research and innovation with data and AI, but also the complicated institutional dynamics of applying these values in real life scenarios. The course also aims to bridge the gap between data ethicists and computing professionals in terms of their values, assumptions, concepts and practices.
Students will learn to engage and lead more effectively on ethical issues with technical colleagues and audiences, and to design ethical interventions which are not only theoretically justified, but practical, comprehensible, and compelling. The aim of the course is to enable you to think critically about the practical methods, skills and techniques for applying data ethics. You'll learn about tools like model cards and datasheets, practice building ethical risk and threat models, and co-develop ethical design briefs and/or ethical research or product reviews. Other topics will include: an ethnography of technological practitioners, ethics educational content in technical degree programmes; incentives in the tech sector; a look at key terms (such as 'intelligence') and the different understandings of them between fields; common evaluation methods for technical systems and their limits. Coursework is focused on exercises that propose and critique ethical data practices directly relevant to data and AI professionals in academic, private and public sectors.
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, 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: None |
| Course Start |
Semester 2 |
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) |
The course will be assessed by means of the following components:
1) Critical Tool Analysis (30%)
1,000 word group assessment. A critical analysis of an ethical tool, policy or practice evaluated it in terms of course themes, considering when the tool is used, who engages with it, its contexts of use, its legacy and concrete examples.
Learning Outcomes Assessed by Component: 1, 2, 3, 4, 5
2) Intervention Design (30%)
2,000 word (or multimodal equivalent) collaborative group assessment. Groups will select an ethical tool, policy or practice and propose a design intervention, drawing on critical analyses of the tool and speculative design methods to imagine alternative designs.
Learning Outcomes Assessed by Component: 1, 2, 3, 4, 5
3) Individual Reflection (40%)
1,500 word individual assessment. Students will reflect on the process of designing 1 and 2 both in terms of what they've learned about practical ethics and in terms of the group work methods and their roles.
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.
Students will receive formative written and verbal group-level feedback on the critical tool analysis presentations during class. Staff will also hold online drop-in sessions to support and guide students on their contributions to the summative assessment. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a discerning understanding of key course concepts - drawing on theories of data ethics and science and technology studies - to think critically about applied ethics and ethical policies and tools.
- Demonstrate an understanding of the culture of computing and data science, including common terminology, education, values, assumptions, concepts, and attitudes towards the ethical dimensions of their work.
- Synthesise and apply a variety of theoretical and empirical material on methods and practices for successfully implementing ethical knowledge and skill in the design, governance and use of data and AI in domains such as health, criminal justice and engineering.
- Clearly and effectively communicate, individually or as part of a team, complex ideas pertaining to the ethical and social dimensions of AI and data-driven technologies.
- Clearly communicate ethical concepts and interventions for technological practitioners, making arguments tailored to be convincing and appealing to this group.
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Reading List
Hagendorff T. The Ethics of AI Ethics: An Evaluation of Guidelines. Minds and machines (Dordrecht). 2020;30(1):99-120. doi:10.1007/s11023-020-09517-8.
Lopez P. Power and Resistance in the Twitter Bias Discourse. Algorithmic Regimes: Methods, Interactions, and Politics. Published online 2023:603-603. doi:10.1145/3593013.3594027.
Sanna J. Ali, Angèle Christin, Andrew Smart, and Riitta Katila. 2023. Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT '23). Association for Computing Machinery, New York, NY, USA, 217-226. https://doi.org/10.1145/3593013.3593990.
Stilgoe, J. 2023. We need a Weizenbaum test for AI. Science 381, eadk0176 DOI:10.1126/science.adk0176.
Mitchell M, Wu S, Zaldivar A, et al. Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency. Published online January 29, 2019. doi:10.1145/3287560.3287596.
Gebru T, Morgenstern J, Vecchione B, et al. Datasheets for Datasets. arXiv.org. Published online December 1, 2021. doi:10.48550/arxiv.1803.09010.
Inioluwa Deborah Raji, Morgan Klaus Scheuerman, and Razvan Amironesei. 2021. You Can't Sit With Us: Exclusionary Pedagogy in AI Ethics Education. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing Machinery, New York, NY, USA, 515-525. https://doi.org/10.1145/3442188.3445914.
Birhane A, Kalluri P, Card D, Agnew W, Dotan R, Bao M. The values encoded in machine learning research. InProceedings of the 2022 ACM conference on fairness, accountability, and transparency 2022 Jun 21 (pp. 173-184).
Moats D, Seaver N. 'You social scientists love mind games': Experimenting in the 'divide' between data science and critical algorithm studies. Big Data & Society. 2019 Mar;6(1):2053951719833404.
Blili-Hamelin B, Hancox-Li L. Making intelligence: Ethical values in iq and ml benchmarks. InProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency 2023 Jun 12 (pp. 271-284). |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Practical Data Ethics,Responsible Research and Innovation,AI Ethics Tools,Translational Ethics |
Contacts
| Course organiser | Dr Morgan Currie
Tel: (0131 6)50 6394
Email: morgan.currie@ed.ac.uk |
Course secretary | Miss Yasmine Lewis
Tel:
Email: yasmine.lewis@ed.ac.uk |
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