Postgraduate Course: Ethical Data Futures (fusion on-site) (EFIE11027)
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 the fundamentals of data ethics as the building blocks of a mature data philosophy. By examining contemporary data practices in their historical, moral and political contexts, students will identify how values in data practice shape our past, present and futures, while developing through collaborative activities six critical data skills required for responsible and ethical data practice. |
| Course description |
This course introduces the fundamentals of data ethics as the building blocks of a mature data philosophy. By examining contemporary data practices in their historical, moral and political contexts, students identify how values in data practice shape our past, present and futures, while developing through collaborative activities six critical data skills required for responsible and ethical data practice. These skills are anchored by theoretical and conceptual tools distributed across the 5 weeks with respect to the following themes:
1) The historical and formative conditions (material/social/political) of contemporary data practices;
2) Moral theories, relations, principles and values used to co-shape, govern and contest contemporary data practices;
3) Political institutions, structures and dynamics of power and justice embedded in contemporary data practices;
4) Emerging challenges and norms of ethical data practice;
5) Data ethics as a collaborative social project of futures-building.
Students will experience a hybrid learning environment taught according to a 'flipped classroom' model centered on critical discourse and collaboration. Each course session will begin with critical, open discussion with instructors of the weekly course reading. Students will submit 2-3 critical reading questions in advance, a selection of which will be shared and discussed in the live classroom session.
This will be followed by discussion of the relevance of the reading to a concrete data scenario or case study that embodies a real-world ethical data challenge or problem of vital importance for the shaping of just and sustainable futures. . These scenarios will have been explored by students over the course of the previous two weeks, in asynchronous and multidisciplinary online learning groups of 10. These online learning groups will receive guidance on how to apply one of the six critical data skills [ethical reflection, analysis, deliberation, evaluation, contestation and decision-making] to each teaching week¿s scenario. Each group¿s collaboration will be captured in a chat transcript for the scenario exercise that will then serve as data for collective analysis and reflection in the following lecture.
Each lecture will end with a whole-class discussion and reflection on the collaboration experience, with course organisers eliciting students' meta-attention to how these skills can be deployed to work with data responsibly and sustainably in any domain.
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 |
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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 2022/23, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
| Course Start Date |
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Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 10,
Online Activities 5,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
83 )
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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 the following components:
1) Summative essay (100%) of 1500-2000 words. Due two weeks after the conclusion of the course (the end of Teaching Week 11).
You will analyse and critically reflect upon the online collaboration transcripts in which you participated during the course. You will integrate these analyses with themes and concepts from the relevant course readings and lectures to identify challenges for, and paths toward, working skillfully, collaboratively, and successfully with others to shape ethical data futures. A fuller brief on the essay requirements will be provided during the course. |
| Feedback |
The online collaboration transcripts will receive group-level feedback at several points during the course, highlighting for students¿ areas where their critical data skills are being successfully deployed and where their application could be strengthened to facilitate the collaborative task. Students will also be encouraged to keep a course notebook/journal in which they record their first-person experiences with the collaboration task and the exercise of their own critical data skills.
This feedback will be formative in strengthening students' metacognitive attention to the collaborative dimension of ethical data challenges. The goal is to move students away from the common but limiting idea of ethics as a private, subjective domain of personal value commitments or preferences, and toward an engagement with data ethics as a maturing social, moral and political practice through which more just and sustainable futures are co-constructed. This will also feed-forward into the final assessment, in which students must analyse the collaboration transcripts as data that can inform the development of more successful collective responses to the ethical challenges facing our shared futures with data. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically reflect with others upon the values and norms that have co-shaped a data practice or artefact.
- Critically analyse with others the morally salient features of a data practice, artefact, or use case, and the various moral choices, risks, benefits, opportunities and challenges it presents for the relevant stakeholders.
- Critically deliberate and evaluate with others the ethical merits of various courses of action (policies, decisions, design choices) available to relevant stakeholders in a data practice, artefact or use case.
- Critically contest, with and to others, a judgment made or course of action proposed or taken in a data-driven context, by articulating moral/ethical considerations that justify the contestation.
- Construct, with others, an ethically justifiable and responsible decision regarding a morally/politically contested data-driven practice, artefact or use case.
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Reading List
Louise Amoore - Cloud Ethics: Algorithms and the Attributes of Ourselves and Others (2020)
Ruha Benjamin - Race after Technology (2019)
Iason Gabriel (2020). "Artificial Intelligence, Values, and Alignment," Minds and Machines 30, 411-437.
Ian Hacking - 'Biopower and the Avalanche of Printed Numbers' (2015)
Anna Lauren Hoffmann (2019), "Where Fairness Fails: Data, Algorithms and the Limits of Antidiscrimination Discourse," Information, Communication and Society 22 (7), 900-915.
Colin McGinn - How We Became Our Data: A Geneology of the Informational Person (2018)
Mohamed, Shakir, Png, Marie-Therese & Isaac, William (2020). "Decolonial AI: Decolonial Theory and Sociotechnical Foresight in Artificial Intelligence," Philosophy and Technology (33), 659-684.
Jacqueline Wernimont - Numbered Lives (2018)
Andrew Whitby - The Sum of the People: How the Census Has Shaped Nations (2020)
Shannon Vallor ¿ Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting (2016)
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Additional Information
| Graduate Attributes and Skills |
This course will incorporate knowledge and understanding of the historical, moral and political contexts of data; skills of practice in applying this knowledge to concrete case studies and contemporary challenges with data; cognitive skills of critical reflection, analysis, and evaluation of data practices,
ICT skills in critically analysing data artefacts and practices, and accountability and collaborative skills of ethical deliberation, contestation and decision-making about case studies in contemporary data practice. |
| Keywords | EFI,Edinburgh Futures Institute,Level 11,PGT,Ethics,Data,Data SKills,Values |
Contacts
| Course organiser | Prof Shannon Vallor
Tel: (0131 6)50 3886
Email: svallor@ed.ac.uk |
Course secretary | Mr Lawrence East
Tel:
Email: Lawrence.East@ed.ac.uk |
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