NLP-CS Syllabus 2026

Please find a description of the syllabus in the attached pdf and below:

INFR11281: Case Studies Syllabus                                                       

Fall 2026/Term 1

21/09/2026 – 30/11/2025

When:                                                Mondays, 12:10 to 2pm, starting September 21st

Where:                                              Appleton Tower - 2.06

Instructor/Course Organiser:     Prof Frauke Zeller, fzeller@ed.ac.uk

TA:                                                      Ella Markham, e.markham@sms.ed.ac.uk

Course Description:

This course will deliver key aspects of the responsible research and innovation (RRI) training provided by the CDT in Designing Responsible NLP. It will build on courses on legal, social, and ethical aspects of AI and NLP that students have taken in year 1.

The 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 in form of real-world AI audits that draw out insights for the development and practical application of principles of responsible NLP. Thus, the course will enable students to practice responsible research and innovation in action.

The course will have an interdisciplinary outlook, and the case studies/audit examples will be provided by the industry partners of the CDT. Example topics include but are not restricted to:

• 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

Coursework:

This is a coursework only course; the students will be assessed on their seminar presentations and on a report of their case study/audit.

There will be two pieces of coursework (detailed information in lecture 1):

1. Seminar presentation on a topic in responsible NLP (35%)

2. Report on an analysis of a case study/AI Audit suggested by a CDT partner (65%)

Learning Outcomes:

On completion of this course, the student will be able to

1. critically evaluate the literature on legal, social, and ethical aspect of NLP
2. working with partners, analyse legal, social, and ethical implications of deploying NLP technology across various application domains
3. design potential solutions to legal, social and ethical problems, combining engineering and design thinking

Course Schedule (subject to adjustments and changes):

Methods of Instruction:

Classes will be instructed in person on a weekly basis. Students are expected to attend each class. Only under reasonable circumstances a student might be able to join online – this must be discussed with the instructor beforehand, ie. at least 24 business hours before the class takes place.

We will be using the Open Course Materials course website to provide updates on the syllabus and other materials: https://opencourse.inf.ed.ac.uk/nlp-cs

We will be using the Learn website of the course for the submission assignment 1 and 2 https://www.learn.ed.ac.uk/ultra/courses/_128272_1/outline

AI Use and Academic Conduct:

No AI use is seen to be necessary in this class. Should groups still want to use AI, they need to discuss this in advance with the course instructor and/or TA.

Please see the School of Informatics guidelines regarding academic misconduct.

Communication Preference:

If questions arise, please check to see if the information you need is 1) in the syllabus; 2) on the assignment sheet; or 3) available from another student in the same section.

Email is one of the main communication channels apart from the weekly sessions. Please make sure to check your university email account every day.

Maintaining a Professional Learning Environment:

• Students are expected to attend classes, unless prevented by a legitimate reason (e.g., illness, death in the family). Non-attendance will adversely impact participation grades, and tends to adversely impact overall academic performance. 

• To reduce disruption, students are expected to arrive on time and stay for the duration of the class. A short break will be given during two-hour classes.

• No electronic recording without instructor permission. 

• This syllabus outlines the intention for course teaching; however, adjustments may be necessary at the discretion of the instructor or due to factors beyond the reasonable control of the instructor. If adjustments are required, students will be advised through in-class discussion and via LEARN and e-mail communication. 

• Students are expected to use an acceptable standard of business communication for all assignments and e-mail communication. 

Literature recommendations:

Ali, S., Kumar, V., & Breazeal, C. (2024). AI Audit: A Card Game to Reflect on Everyday AI Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15981-15989. https://doi.org/10.1609/aaai.v37i13.26897

Ayling, J., Chapman, A. Putting AI ethics to work: are the tools fit for purpose?. AI Ethics 2, 405–429 (2022). https://doi.org/10.1007/s43681-021-00084-x 

Brundage, M., Dreksler, N., Homewood, A., McGregor, S., Paskov, P., Stosz, C., ... & Tovcimak, R. (2026). Frontier ai auditing: Toward rigorous third-party assessment of safety and security practices at leading ai companies. arXiv preprint arXiv:2601.11699.

Koshiyama A et al. 2024 Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms. R. Soc. Open Sci. 11: 230859. https://doi.org/10.1098/rsos.230859 

Lacmanovic, S., & Skare, M. (2025). Artificial intelligence bias auditing–current approaches, challenges and lessons from practice. Review of Accounting and Finance, 24(3), 375-400. https://doi.org/10.1108/RAF-01-2025-0006

Laine, J., Minkkinen, M., & Mäntymäki, M. (2024). Ethics-based AI auditing: A systematic literature review on conceptualizations of ethical principles and knowledge contributions to stakeholders. Information & Management, 61(5). https://doi.org/10.1016/j.im.2024.103969

Li, Y., & Goel, S. (2025). Making it possible for the auditing of AI: A systematic review of AI audits and AI auditability. Information Systems Frontiers, 27(3), 1121-1151. https://doi.org/10.1007/s10796-024-10508-8

Manheim, D., Martin, S., Bailey, M. et al. The necessity of AI audit standards boards. AI & Soc (2025). https://doi.org/10.1007/s00146-025-02320-y 

Mökander, J. Auditing of AI: Legal, Ethical and Technical Approaches. DISO 2, 49 (2023). https://doi.org/10.1007/s44206-023-00074-y

Mökander, J., Floridi, L. Ethics-Based Auditing to Develop Trustworthy AI. Minds & Machines 31, 323–327 (2021). https://doi.org/10.1007/s11023-021-09557-8

Mökander, J., Floridi, L. Operationalising AI governance through ethics-based auditing: an industry case study. AI Ethics 3, 451–468 (2023). https://doi.org/10.1007/s43681-022-00171-7

Murikah, W., Nthenge, J.K., & Musyoka, F.M. (2024). Bias and ethics of AI systems applied in auditing – A systematic review. Scientific African, 25. https://doi.org/10.1016/j.sciaf.2024.e02281

Ojewale, V., Steed, R., Vecchione, B., Birhane, A., & Raji, I. D. (2025, April). Towards AI accountability infrastructure: Gaps and opportunities in AI audit tooling. In Proceedings of the 2025 CHI conference on human factors in computing systems (pp. 1-29). 

Schiff, D. S., Kelley, S., & Camacho Ibáñez, J. (2024). The emergence of artificial intelligence ethics auditing. Big Data & Society, 11(4). https://doi.org/10.1177/20539517241299732 (Original work published 2024)