Postgraduate Course: Case Studies in AI Ethics (CSAI) (INFR11206)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||Artificial 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.
In this course, we will discuss the following topics:
- Deployed AI technologies
- Ethical and social issues arising with data
Fairness, Accountability and Transparency:
- Overview of the definitions
- Types of bias
- 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)
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.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2021/22, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||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.
||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.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||Case Studies in AI Ethics (CSAI) (INFR11206)||2:00|
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.
|Required readings will be primarily from open access papers listed on the course website. 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)
|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)
|Keywords||Artificial intelligence,ethics,machine learning,data science,CSAI
|Course organiser||Dr Nadin Kokciyan
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701