IS-508 Human-Centered Artificial Intelligence

The course is connected to the following study programs

• Master's Programme in Information Systems

Teaching language

English

Recommended prerequisites

Master level course on applied data science is recommended, e.g. IS-427 or corresponding.

Course contents

The course is based on concepts and methods from several fields, including computer science, mathematics, political science, law studies, sociology, business economics, management, and philosophy. During the course we will explore representative work in this field, spanning the above disciplines and the students will learn to use related concepts and techniques. Students will work on assignments related to real-world problems.

The course will give students a systematic basis for addressing trustworthiness, human control, fairness, intelligibility and accountability in Artificial Intelligence building a bridge between principles and practice.

Learning outcomes

Upon completion of the course, students should:

• have knowledge and be able to define key aspects of human-centered Artificial Intelligence including trustworthiness, human control, fairness, intelligibility and accountability.
• be able to discuss why human-centeredness is needed for Artificial Intelligence and the importance of responsible Artificial Intelligence use (RAI).
• have knowledge on how to design and evaluate human-centered Artificial Intelligence.
• have knowledge of different Artificial Intelligence techniques and approaches and be able to use advanced platforms for Artificial Intelligence models (AutoML).
• be able to assess the fitness of Artificial Intelligence for solving different problem types and the related ethical challenges.
• be able to identify the positive and negative potentials of Artificial Intelligence for sustainability.
• be able to facilitate communication between management, technical teams and regulators.

Examination requirements

Approved mandatory submissions and attendance at mandatory presentations. More information is provided in Canvas.

Teaching methods

The teaching consists of a combination of lectures, group work and course assignments including student presentations. There is a compulsory attendance in parts of the teaching, this will be specified at the start of the semester. Expected working hours are 270 hours.

Evaluation

The person responsible for the course decides, in cooperation with the student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Assessment methods and criteria

An individual written exam that counts for 60% and a group report that counts for 40%. Both parts must be passed. Grades A-F.