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DRPS : Course Catalogue : Edinburgh Futures Institute : Edinburgh Futures Institute

Postgraduate Course: Data Ethics as a Practice (fusion online) (EFIE11162)

Course Outline
SchoolEdinburgh Futures Institute CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryHow should we apply 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 ethical frameworks guide responsible and sustainable research and uses of data and AI in applied settings and workplaces? In this course, we use case studies 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 takes as its frame the role of data and AI in practical ethics, as a domain of skilled and intelligent action. In this frame, ethics and data are not separate but intertwined by technique-ethics as a social body of techniques for living well together, and techniques of data-driven science and engineering as socially constructed means of building values into our future lived worlds. We consider the relationship of data and AI to ethics in the practical domains of engineering, criminal justice and healthcare. In what ways are data and algorithmic systems essential to our practices of designing and delivering safe environments, just sentences, or human health? Conversely, we examine how ethical values, virtues and duties of care can guide responsible and sustainable research and innovation with data and AI, for example: justice, fairness, privacy, safety, integrity, accountability, humility and respect for human dignity. The aim of the course is to enable you to acquire practical methods, skills and techniques for accomplishing this alignment, individually and in teams. 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. Coursework is focused on case studies that explore ethical data practices and challenges directly relevant to data and AI professionals in academic, private and public sectors.

This course will be taught in a hybrid and intensive format starting with an asynchronous flexible learning 'immersion' phase. Here you will explore texts and videos that introduce you to key concepts, methods and techniques for ethical practices that foster responsible data and AI research and innovation, and work with others to analyse a concrete case study in preparation for the intensive phase. This is followed by a 2-day intensive 'fusion' workshop where you will benefit from a mix of lectures, group exercises and reflection activities designed to integrate and strengthen your practical skills in collaborative data/AI ethics. This session is followed by a post-intensive application phase where you will work in small groups to apply what you have learned to a new case.

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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Course Start Date 15/01/2024
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 5, Seminar/Tutorial Hours 9, Other Study Hours 5, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 79 )
Additional Information (Learning and Teaching) Other Study: Scheduled Group-work Hours (hybrid online/on-campus) - 5
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Summative Assessment:

The course will be assessed by means of the following assessment components:

1) Group Presentation (20%)

During the immersive phase, students will work in groups to build an ethical 'threat model' of the 'data debacle' case study, which the group will write up together (1000 words) and present in class during the Day 1 intensive session.

2) 500 Word Contribution to Collaborative Design Brief / Model Card / Ethical Product Review (40%)

During the post-intensive application phase, groups will produce a collaborative design brief, model card or ethical product review (2000 words); students will be marked according to their own 500-word contribution to the document (40%), due four weeks after the conclusion of the intensive workshop.

3) 750 Word Critical 'Post-Mortem' (40%)

Students will also be assessed by an individual 750-word critical 'post-mortem' (40%) due one week later that elicits personal reflection on the collaboration experience, its outcomes, and its wider implications for effective data/AI practice in organisations and teams.
Feedback Students will receive formative written and verbal group-level feedback on the threat model presentations during the intensive session. Staff will also hold online drop-in sessions during the application phase to support and guide students on their contributions to the summative assessment. Written feedback will be provided on both portions of the summative assessment.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate a discerning understanding of key course concepts drawing on theories of data ethics, data justice, design justice, and responsible research and innovation as supported by reading relevant literature.
  2. 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.
  3. Collaborate and deliberate effectively with peers in a team context, by applying group-level tools for ethical analysis, reasoning, and choice to case studies relevant to the course.
  4. Clearly and effectively communicate, in practice-oriented tasks, complex ideas pertaining to the ethical and social dimensions of AI and data-driven technologies.
  5. Exercise autonomy, accountability and leadership in working together with other students to develop a practical intervention for improving the ethical impact of a data-driven process or artefact.
Reading List
Essential Reading:

Costanza-Chock, Sasha. 2018. 'Design Justice: Towards an Intersectional Feminist Framework for Design Theory and Practice'. In .

Friedman, B. & Hendry, D.G. (2019) Value Sensitive Design. MIT Press, Cambridge. (selections)

Hagendorff, Thilo. 2020. 'The Ethics of AI Ethics: An Evaluation of Guidelines'. Minds and Machines 30 (1): 99-120.

Macnaghten, P. (2020) The Making of Responsible Innovation. Cambridge University Press, Cambridge. (selections)

Mitchell, Margaret, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. 'Model Cards for Model Reporting'. Proceedings of the Conference on Fairness, Accountability, and Transparency, January, 220-29.

Stilgoe, J. & Guston, D. (2016) Responsible Research and Innovation. In The Handbook of Science and Technology Studies (4th Edition), (Eds, Felt, U. et al.) MIT Press, Cambridge,

Recommended Reading:

Barredo Arrieta, Alejandro, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, et al. 2020. 'Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI'. Information Fusion 58 (June): 82-115.

Alberdi, Eugenio, Peter Ayton, Andrey Povyakalo, and L. Strigini. 2005. 'Automation Bias and System Design: A Case Study in a Medical Application'. In , 2005:53-60.

Arnold, Mark Henderson. 2021. 'Teasing out Artificial Intelligence in Medicine: An Ethical Critique of Artificial Intelligence and Machine Learning in Medicine'. Journal of Bioethical Inquiry, January, 1-19.

Candy, S. and Potter, C (2019) Design and futures. Tamkang University Press, Taipei.

Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2020. 'Datasheets for Datasets'. ArXiv:1803.09010 [Cs], March.

Joly, Pierre-Benoit. 2015. 'Governing Emerging Technologies? The Need to Think Outside the (Black) Box'. In Science and Democracy. Routledge.

McNamara, Andrew, Justin Smith, and Emerson Murphy-Hill. 2018. 'Does ACM's Code of Ethics Change Ethical Decision Making in Software Development?' In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 729-33. ESEC/FSE 2018. New York, NY, USA: Association for Computing Machinery.

Morley, Jessica, Luciano Floridi, Libby Kinsey, and Anat Elhalal. 2019. 'From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices'. ArXiv:1905.06876 [Cs], September.

Selin, C. et al. (2015) Scenarios and design: Scoping the dialogue space. Futures, 74, 4-17.


Vallor, Shannon. 2018. 'An Ethical Toolkit for Engineering/Design Practice', Markkula Center for Applied Ethics.
Additional Information
Graduate Attributes and Skills 1) Students will develop key theoretical knowledge and critical understanding through readings, discussion, case study analysis and reflections on core texts (SCQF characteristic 1 & 2).

2) Students will gain cognitive skills by conducting original research on case studies, analysed in relation to the course literature and themes (SCQF characteristic 2).

3) Students will develop communication skills by interacting with academic staff and their peers in group collaborative activities and exercises, and developing usable, practice-oriented outputs for a non-academic audience (SCQF characteristic 3 & 4)

4) Students will gain autonomy, accountability and learn to work with others by collaborating in small groups on the case study and during the preparation stage of their project, developing their communication skills, and gaining valuable skills in working with others (SCQF characteristics 3& 4).
KeywordsPG,Level 11,EFI,Data,Artificial Intelligence and Ethics,Practical Data Ethics,Responsible Research
Course organiserDr Robert Smith
Tel: (0131 6)504 258
Course secretaryMiss Veronica Silvestre
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