Postgraduate Course: Case Studies in Responsible Natural Language Processing (INFR11300)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course will focus on applying responsible NLP principles in practice. This will take two forms: (1) students will report and reflect on their year 1 learning in the area of legal, social, and ethical aspects of AI and NLP, (b) students will work on case studies that draw out insights for the development and practical application of principles of responsible NLP. For (1), students will work in teams to undertake peer-led teaching in the form of student run seminars. For (2), the students will work in teams to apply their learnings into an ethics assessment with non-
academic partners.
This course is ONLY available to students on the second year of the PhD with Integrated Study in Designing Responsible Natural Language Processing. |
| Course description |
This course will enable students to practice responsible research and innovation in action. They will reflect on their learning in areas of legal, social, and ethical aspects of AI and NLP and put this learning into practice by working on case studies on responsible NLP. The course will have an interdisciplinary outlook, and the case studies will be provided by the industry partners of the CDT. Example topics include:
- fairness and bias
- social issues of model deployment
- impact of AI and NLP technology on the workplace
- data privacy, copyright, and other legal implications of NLP
- translation of ethical and moral values to technical systems
- political influence and manipulation with the help of AI and NLP
- generative AI and the creative industries
The students will engage in two main activities: (1) student-led seminars in which students present topics in responsible NLP based on the courses they've taken in year 1; (2) partner presentations introducing case studies on responsible NLP; students will work in small teams which will select one of these case studies and work with the partner on an analysis of that case study, drawing on their knowledge from the first part of the course.
This is a course-work only course; the students will be provided formative feedback based on their seminar presentations and assessed via a pass/fail grade, and further assessed through a report of their case study analysis.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course is ONLY available to students on the second year of the PhD with Integrated Study in Designing Responsible Natural Language Processing. |
Course Delivery Information
|
| Academic year 2026/27, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 1 |
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 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Feedback |
Students will receive formative feedback in-class on their seminar presentations, and continuous feedback from the course instructor and the industry partner they work with on their case study analysis. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- critically evaluate and communicate the legal, social, and ethical considerations of responsible NLP
- analyse legal, social, and ethical implications of deploying NLP technology across various application domains
- identify and design potential solutions to legal, social and ethical problems, and provide usable recommendations for non-academic organisations to practice responsible NLP
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Additional Information
| Course URL |
https://opencourse.inf.ed.ac.uk/nlp-cs |
| Graduate Attributes and Skills |
Research and enquiry: problem-solving, critical/analytical thinking, knowledge integration.
Personal effectiveness: planning and organizing, influencing positively.
Personal responsibility and autonomy: ethics and social responsibility, self-awareness and reflection, decision-making.
Communication: cross-disciplinary communication, interpersonal/teamwork skills. |
| Keywords | Natural language processing,Research skills,Responsible research and innovation,Responsible AI |
Contacts
| Course organiser | Prof Frauke Zeller
Tel:
Email: fzeller@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 5194
Email: lindsay.seal@ed.ac.uk |
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