Admission requirements
Enrolment is restricted to a limited number of Master students, who preferably follow other courses related to artificial intelligence (including deep learning, machine learning, optimisation, planning and scheduling, multi-agent systems and other areas of AI).
Assumed prior knowledge
It is assumed that the student has
Good knowledge of Artificial Intelligence and Machine Learning techniques (see for example the content of the courses 'Data Science', 'Machine Learning' and 'Symbolic AI')
Familiarity with deep learning (see 'Introduction to Deep Learning' -- should be followed prior to this course)
Students are discouraged to follow this course in the first semester of the program
Description
While in the past, performance has been the main focus of much work in artificial intelligence (AI), aspects of trustworthiness safety are increasingly recognised as similarly significant.
This seminar course on trustworthy artificial intelligence aims to develop a deeper insight into various aspects of trustworthy AI.
Students will work in groups of two on a range of topics from trustworthy artificial intelligence, which spans the robustness and verification of AI methods, including - but not restricted to - neural networks; the explainability and interpretability of the results obtained from AI algorithms; as well as bias and privacy issues in machine learning.
Each group will be assigned recently published work from the research literature, which will serve as the starting point for an in-depth investigation of a specific topic; the results of this investigation will be presented in class and compiled into a report. By presenting the results of the topic in class, other students will be informed about the findings, and learn about all topics that are in scope.
Course objectives
The first lecture will be an introduction by the lecturer. Afterwards, the students will be divided into groups of two, where they will work on a certain topic related to trustworthy artificial intelligence.
After the course, students should be able to
understand the various topics and aspects of trustworthy artificial intelligence (in particular the seven aspects formulated by the European Commission)
critically evaluate the literature on a specific topic from trustworthy artificial intelligence
apply existing methods to novel problem instances (in particular formal verification of robustness properties)
disseminate obtained knowledge in a comprehensible way (using the following forms: presentation, interaction and report writing)
See Bloom’s taxonomy for a further explanation of the required level of understanding per item.
Timetable
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Seminar (content will be provided through student presentations & discussions)
Final conference (on which we will present the results to each other)
Course load
Total hours of study: 168 hrs. (= 6 EC)
The time will be divided between:
Preparing the lectures
- read the papers from the literature to engage in discussions
- once: prepare to present a paper
Attending the lectures (mandatory)
Peer review, giving critical but constructive feedback to fellow-students
Final conference, on which the results will be presented
Group work is an integral part of the course. You will be expected to complete the assignments together with a team mate.
Assessment method
The students will write a scientific report that surveys the literature on a selected topic from the field of trustworthy artificial intelligence, accounting for 80% of the grade. Throughout the semester, students can obtain feedback on their progress. It will be presented to their fellow students at the final conference (20% of the grade). In both cases, an important aspect of the grade is the comprehensibility to fellow students. The final report should have a passing grade in order to complete the course.
Reading list
Recent and seminal overview papers from the Trustworthy Artificial Intelligence literature.
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Lecturers: dr. J.N. van Rijn
Remarks
More information will be available on the dedicated website for this course, hosted on BrightSpace.
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.