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| Title |
Elective course: Responsible AI
|
| Semester |
E2024
|
| Master programme in |
Computer Science / Digital Transformation
|
| Type of activity |
Course |
| Teaching language |
English
|
| Study regulation |
Read about the Master Programme and find the Study Regulations at ruc.dk |
| REGISTRATION AND STUDY ADMINISTRATIVE | |
| Registration |
You register for activities through stads selvbetjening during the announced registration period, which you can see on the Study administration homepage. When registering for courses, please be aware of the potential conflicts and overlaps between course and exam time and dates. The planning of course activities at Roskilde University is based on the recommended study programmes, which should not overlap. However, if you choose optional courses and/or study plans that goes beyond the recommended study programmes, an overlap of lectures or exam dates may occur depending on which courses you choose. |
| Number of participants |
|
| ECTS |
5
|
| Responsible for the activity |
Jens Ulrik Hansen (jensuh@ruc.dk)
|
| Head of study |
Henrik Bulskov (bulskov@ruc.dk)
|
| Teachers |
|
| Study administration |
IMT Registration & Exams (imt-exams@ruc.dk)
|
| Exam code(s) |
U60596
|
| ACADEMIC CONTENT | |
| Overall objective |
The purpose of elective courses is to give the student opportunitities to specialize within a specific subject area, where the student acquires knowledge, skills and competences in order to translate theories, methods and solutions ideas into their own practice. |
| Detailed description of content |
Artificial Intelligence (AI) as a technology is seeing a rapid increase of use-cases across all domains. While many of these applications of AI help advance humanity and help us in our daily lives, there are also a growing number of cases that are deeply concerning such as AI used to derive sentences in the court of law, profile socially disadvantaged, or surveil citizens, as well as numerous cases of AI systems being biased against minority groups. Thus, although the benefits of implementing AI systems are to great not to give up the technology, we need to be really careful in choosing which AI systems to develop and how to develop them – we need Responsible AI! In this course, we will look both at the challenges of AI systems, as mentioned above, but also at possible responsible ways to mitigate these challenges. More specifically, potential topics covered are: Ethics and AI, Accountability and Responsibility, Transparency and explainability, The data science process and the epistemology of data science, Responsibility in practice, Bias and Fairness, Privacy and legislation, Misinformation and democracy, Responsibility of Generative AI, Human-centric AI design, and Responsible AI in healthcare. Note that, the course is not an introductory course to artificial intelligence and basic knowledge of central AI concepts are assumed – however, it is possible to individually catch up on such concepts during the beginning of the course. Moreover, no programming skills are assumed and the course is suited for students from multiple educational programs such as Computer Science, Digital Transformation, Journalism, or Philosophy. |
| Course material and Reading list |
to be announced on Moodle |
| Overall plan and expected work effort |
The course will have the form of in-person seminars with an active learning style, i.e., introduction to theory in combination with hands-on in-class exercises and discussions based materials read in advance. Students are expected to read the required material before class and to show up physically and take part in the discussions and exercises. Study effort: The course's 5 ECTS correspond to a total of 135 hours workload with:
|
| Format |
|
| Evaluation and feedback |
Evaluation form to be filled out (anonymously) plus open discussion during the course. |
| Programme |
|
| ASSESSMENT | |
| Overall learning outcomes |
After completing this course, students will be able to:
|
| Form of examination |
Individual oral exam without time for preparation
Time allowed for exam including time used for assessment: 20 minutes. Permitted support and preparation materials: All. Assessment: 7-point grading scale Moderation: Internal co-assessor. |
| Form of Re-examination |
Samme som ordinær eksamen / same form as ordinary exam
|
| Type of examination in special cases |
|
| Examination and assessment criteria |
The assessment will be based on the extent to which the student can:
|
| Exam code(s) | |
| Last changed | 11/03/2024 |
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