Undergraduate Course: Ethics and Politics of Data (EFIE08004)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | The story of data helps us better understand the growing power of data in today's world. This course asks the question 'what are data, and how do they come to be?' Working collaboratively, students from across the disciplines will uncover the moral and political values that shape human practices of counting, measuring, and labelling reality, in the process developing the foundational skills of critical and responsible data practice. |
| Course description |
This course asks and answers the question 'what are data, and how do they come to be?' The story of data reveals the moral and political values that shape human practices of counting, measuring, and labelling reality, and helps us better understand the growing power of data in today's world. Designed to engage students across the disciplines, this introductory course offers a foundational integration of basic concepts and methods of data science with the historical and philosophical context that reveals their ethical and political dimensions as inseparable from their scientific value. The course draws from the disciplines of philosophy, sociology, history, mathematics, computer science and the design arts to build up a more comprehensive picture of how data are constructed, interpreted, shared and used for a growing range of scientific, commercial, public and creative purposes.
The course content begins with a general introduction to the concept of data and its historical origins and uses, building up to an outline of the core data concepts and methods increasingly that increasingly shape contemporary society. In the second course phase, ethical and political theories of justice, power, identity, consent, and rights are used to further contextualise our relationships to data. The third phase reveals how naïve views about the relationship between data and reality have often led to failed scientific endeavours and harmful social and political outcomes. In the fourth and final phase, students use the conceptual tools they have acquired to practice identifying and correcting these limited perspectives on data. (These four phases are overlapping and enmeshed, rather than cleanly demarcated; the progression represents the dominant theme/emphasis of each course phase). Throughout the course, 2 hour seminars are structured to accomplish 1) short (30-40 min) lectures on historical, moral and political context of a data practice; 2) an active discussion (45 minutes) of theoretical frames, concepts and critical tools from the reading as they apply to the practice; 3) presentation and discussion (30 min) of an artefact, technique or case study related to the data practice that will be the subject of an autonomous learning group task (1 hour weekly).
By engaging in discussion-centered seminars and working collaboratively in autonomous learning groups on well-defined critical and analytical tasks related to the seminar content, students will experience the transformation from a passive understanding of data and a data-driven society, to a habit of active, collaborative and critical engagement with data and how they are constructed and used to promote particular social and political values, goals and arrangements. This habit is the cornerstone of the mature data philosophy that students will build throughout their EFI journey, as they acquire and practice the critical data skills needed for the responsible and trustworthy use of data across a wide range of academic and professional settings.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2021/22, Available to all students (SV1)
|
Quota: 40 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 22,
Other Study Hours 11,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
163 )
|
| Additional Information (Learning and Teaching) |
11 hours scheduled group-work
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Feedback |
Feedback will be in the form of written comments on assignments, with the option for students to attend office hours for further individualised feedback.
Feedback for assessment 1 will feed forward to assessment 2 (the final) in showing where the moral and political dimensions and their implications might have been more effectively identified or analysed, so that the student is encouraged to reflect more fully in preparation for the final assessment upon how the ethical and political implications of data shape individual outcomes, events, trends and institutions. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Explain and give examples of how ethical and political factors have shaped particular historical and contemporary data practices.
- Identify and distinguish relevant ethical and political dimensions across a range of emerging data contexts and practices, for example, through analysis of a novel research study, commercial technology, data-driven scientific technique or local government initiative.
- Recognize basic concepts and widely used methods of qualitative and quantitative data collection, labelling, analysis, interpretation, presentation and use, and describe how and where ethical and political values and structures may be implicitly or explicitly embedded within these.
- Deploy critical thinking and collaboration skills to interrogate and test specific assumptions, methodologies, or artefacts that may reveal naïve, skewed, or incomplete understandings of data, assess their potential harms to scientific research and/or to society, and collectively envision potential mitigating/corrective steps to improve data understanding and practice.
- Identify and reflect upon key value tensions, unresolved conflicts and ambiguities surrounding our historical, moral and political relationships with data; for example: open questions about data ownership, rights, justice and public good, the complex relationship between data bias and objectivity or 'ground truth', and the future role of data in addressing complex social challenges such as climate change, public health, ecological degradation, and rising inequality.
|
Reading List
Louise Amoore - Cloud Ethics: Algorithms and the Attributes of Ourselves and Others (2020)
Ruha Benjamin - Race after Technology (2019)
Caroline Criado Perez - Invisible Women: Exposing Data Bias in a World Designed for Men (2020)
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 Koopman - 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) |
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 and collaborative skills in critically analysing data artefacts and practices in learning groups. |
| Keywords | Data ethics,justice,human rights,values,ground truth,bias,power,representation,data models,imaging |
Contacts
| Course organiser | Prof Shannon Vallor
Tel: (0131 6)50 3886
Email: svallor@ed.ac.uk |
Course secretary | Miss Katie Murray
Tel:
Email: Katie.murray@ed.ac.uk |
|
|
University Homepage