Admission requirements
Students need to be registered for the minor AI and Society to follow this course.
Students of all faculties can register for the minor.
Description
The best way to develop familiarity with a technology is to work with it rather than to talk about it. With this in mind this course offers a hands-on approach to AI; aimed at equipping students with digital skills needed to understand and work with AI technologies. This course lets students gain experience with coding and developing/implementing simple AL and ML algorithms – particularly in the sphere of natural language processing (NLP). During hands-on sessions and "class adventures" students experience how the design and development of these technologies is structured; and they will benefit from working together in multidisciplinary teams. Typical evaluation considerations such as accuracy, different forms of bias and ways of addressing them, are discussed and evaluated. Students will learn through interacting with each other how different perspectives can be applied to technology development.
Depending on group size; the class may be split for some of the lab-sessions.
Course objectives
This project-oriented course aims to let students develop hands-on experience with text-based AI systems, to develop insights on strengths and limitations, and to connect those technical insights to societal impacts. At the same time students should get some feeling for the platforms, processes, resources etc. involved in the production of AI-systems.
The course will contain group work. By combining different disciplinary backgrounds in each of the project teams the course aims to foster interdisciplinary communication and understanding of technical and societal aspects of AI.
At the end of this course, students will be able to:
Develop their digital skills regarding simple coding and reading (pseudo)code (for students with no background in technology);
Demonstrate translating technical concepts to general audiences;
Explain their disciplinary perspective on technical and societal aspects of AI to students from other disciplines;
Combine different disciplinary perspectives on technical and societal aspects of AI;
Get a practical understanding of simple algorithmic decision tools and their societal implications.
Timetable
Check MyTimetable.
Mode of instruction
Lectures
Number of (2 hour) lectures: 7
Names of lecturers: Dr. Jann Tosatto
Required preparation by students: see Brightspace
Other methods of instruction
Description: Lab sessions and final presentations
Number of (2 hour) instructions: 3
Names of instructors: to be confirmed
Required preparation by students: see Brightspace
Note:
In person attendance and active participation is a cornerstone element of the course and is required to successfully complete the group work project assignment component.
Assessment method
Examination form(s)
Reflective journals (20%);
Coding assignment (20%);
Group work assignment (project and presentation) (60%).
The assessment of this course will consist of three practical assignments: one reflective journal (total weight 20%), a coding assignment (20%) and a group work assignment (60%). The reflective journals and the coding assignment are mandatory to participate in the final group assignment.
For each part, the assignment must be successfully completed with a passing grade (5,50).
If the course is not completed with a passing grade within the academic year, any partial grades obtained will expire by the end of the academic year.
Submission procedures
See Brightspace.
Areas to be tested
The material relevant to the examination elements consists of the required reading (literature) and practical exercises for the course, the course information guide and the subjects taught in the lectures, the seminars and all other instructions which are part of the course.
Resit, review & feedback
This course consists of practical assignments, so no regulations pursuant to examination re-sits apply. With that said,
The group work (60%) is not eligible for retake.
The retake deadline for the reflective journal (total weight 20%) and the coding assignment (20%) will be during examination week.
Reading list
Obligatory course materials See Brightspace.
Registration
Registration for courses and exams takes place via MyStudymap. If you do not have access to MyStudymap (guest students), look here (under the Law-tab) for more information on the registration procedure in your situation.
Contact
Coordinator: Dr. Jann Tosatto
Work address: Kamerlingh Onnes Building, Steenschuur 25
Contact information:
Telephone number: +31 71 527 2727
Institution/division
Institute: Metajuridica
Department: eLaw
Telephone number secretary: 071 – 527 8838
Email: elaw@law.leidenuniv.nl