Postgraduate Course: Case Studies in AI Ethics (CSAI) (UG) (INFR11231)
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 | 10 |
ECTS Credits | 5 |
| Summary | This course follows the delivery and assessment of Case Studies in AI Ethics (CSAI) (INFR11206) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11206 instead. |
| Course description |
This course follows the delivery and assessment of Case Studies in AI Ethics (CSAI) (INFR11206) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11206 instead.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Case Studies in AI Ethics (CSAI) (INFR11206) OR
Professional Issues (Level 10) (INFR10022) OR
Ethics of Artificial Intelligence (PHIL10167)
|
Other requirements | Priority is given to students enrolled on Artificial Intelligence programmes; students enrolled on other programmes are recommended to get Course Organiser approval to ensure enrolment approval.
This course follows the delivery and assessment of Case Studies in AI Ethics (CSAI) (INFR11206) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11206 instead. |
Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: 5 |
| Course Start |
Semester 2 |
| Course Start Date |
13/01/2025 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 18,
Seminar/Tutorial Hours 2,
Feedback/Feedforward Hours 2,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
74 )
|
| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Exam 60%
Coursework 40%
Assessment will be a combination of groupwork and individual assessment. Groups will choose one case study from a list of case studies and provide an outline to be implemented during their individual assessment. |
| Feedback |
Formative feedback will be provided during class discussions, tutorials and the first group-based coursework. Summative feedback on assessments will be provided in line with the current School of Informatics guidelines. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Case Studies in AI Ethics (CSAI) PG INFR11206 (UG) (INFR11231) | 2:120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- understand data ethics and arising issues (e.g. bias, fairness, privacy) in AI systems
- explain and provide examples of how AI systems can play a critical role in decision making
- analyse case studies to identify and mitigate potential risks considering legal, social, ethical or professional issues
- apply ethical methodologies in the design of responsible AI systems
|
Reading List
A representative reading list is as follows:
Lin, P., Abney, K. and Jenkins, R. "Robot Ethics" 2.0, Oxford University Press (2019)
Wallach, W., Allen, C. "Moral Machines", Oxford University Press (2009)
Dignum, Virginia. "Responsible artificial intelligence: designing AI for human values" (2017)
Boddington, Paula. "Towards a code of ethics for artificial intelligence". Cham: Springer (2017) |
Additional Information
| Course URL |
https://opencourse.inf.ed.ac.uk/csai |
| Graduate Attributes and Skills |
Cognitive skills: problem-solving (via tutorials, coursework), critical thinking (via lectures / tutorials / coursework), handling ambiguity (via in-class discussions)
Responsibility, autonomy, effectiveness: independent learning (via readings, videos), self-awareness and reflection (via tutorials, coursework, lectures), leadership (via discussions about case-studies in small groups), time management (via coursework, discussions during classes), ethical / social / professional awareness and responsibility (via tutorials, lectures, coursework, readings, videos)
Communication: written communication (via coursework), verbal communication (via in-class discussions) |
| Additional Class Delivery Information |
The students will be expected to prepare for the lectures by reading papers, news; or watching videos. Some lectures will include case studies where students will discuss the ethical issues in small discussion groups for 15 minutes; and report back their findings. |
| Keywords | artificial intelligence,ethics,machine learning,data science,CSAI |
Contacts
| Course organiser | Dr Nadin Kokciyan
Tel:
Email: nadin.kokciyan@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 5194
Email: lindsay.seal@ed.ac.uk |
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