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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Case Studies in AI Ethics (CSAI) (INFR11206)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryArtificial intelligence (AI) is being deployed in real-world settings more than before. Especially, fully automated AI systems started to make critical decisions such as who should be employed or who is a criminal. In this course, the students will increase their understanding of data ethics.

The course gives an overview of the ethical issues (e.g. bias, fairness, privacy) and brings together different case studies from various contexts. The students will analyse case studies to identify and mitigate potential risks considering legal, social, ethical or professional issues.
Course description In this course, we will discuss the following topics:

Data Ethics:
- Deployed AI technologies
- Ethical and social issues arising with data

Fairness, Accountability and Transparency:
- Overview of the definitions
- Types of bias
- Explainability

- Arising issues (e.g. surveillance, usability vs privacy trade-off)
- State of the art: ML approaches, Agent-based approaches

Towards implementing ethical tools:
- Implementing AI Ethics
- Ethics guidelines for Trustworthy AI (e.g. European Commission), AI Auditing guidelines (e.g. ICO)
- Applied Ethics (e.g. IEEE Ethics in Action, Markkula Centre's Ethics Toolkit)
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) (UG) (INFR11231) 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.

MSc students must register for this course, while Undergraduate students must register for INFR11231 instead.
Course Delivery Information
Academic year 2024/25, Not available to visiting students (SS1) Quota:  55
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:
  1. understand data ethics and arising issues (e.g. bias, fairness, privacy) in AI systems
  2. explain and provide examples of how AI systems can play a critical role in decision making
  3. analyse case studies to identify and mitigate potential risks considering legal, social, ethical or professional issues
  4. 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
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.
KeywordsArtificial intelligence,ethics,machine learning,data science,CSAI
Course organiserDr Nadin Kokciyan
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 2701
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