Responsible Design of Interactive AI-Systems, 7.5 Credits

Contents

This advanced level course is aimed to deepen knowledge and skills in design methodology for responsible development of interactive, intelligent systems and environments with focus on  human-centered artificial intelligens (AI), and in theories for Human-Computer Interaction (HCI) theory and new technology for interacting and collaborating with intelligent software agents and environments. The course builds upon research and theories developed in the intersection of AI and HCI, with special focus on socio-technical systems embedding AI, human-AI collaboration and related ethical and social aspects. Some of the core questions in focus during the course will be

1. How are we going to collaborate with AI technology in the future?
2. What happens with control and autonomy when the system learns and changes behaviour over time?
3. How do we design an interactive, intelligent system in such a way that we embed into the system mechanisms for weighing together different stakeholders values in multicultural contexts (e.g. moral and ethics), as well as explaining its reasoning and guarantee transparency? and
4. How do we embed a continuing responsible and participatory design process throughout the use of intelligent and collaborative systems?

The course is divided into the following two parts:
Module 1: Theory and methodology (4,5 ECT)
The theoretical part covers theoretical frameworks and methods for designing and evaluating interactive intelligent systems, and adopts a responsible AI perspective. The socio-technical systems perspective will be applied throughout the course with theories and methods supporting the development of socially intelligent systems. This includes human-centric factors vital when developing AI-based interactive systems, such as the effect of use, mechanisms for human development, motivation, behavior change, autonomy, empowerment, competence, relatedness, value for the individual and society, responsibility, accountability and ethics.

Methodology includes in particular responsible AI design, participatory action research approaches and activity-centered design. The theory and methodologies are applied in group exercises and in an interdisciplinary project in Module 2.
 
Module 2: Practical application of theory and methods in a project (3 ECT)
Theories and methods brought up in Module 1 are applied in a group project, which is conducted in parallel with the theoretical part. The project topic is motivated by a societal challenge and focuses on engaging potential end users and other stakeholders in an interdisciplinary participatory design process. Design activities can be done in labs with technology and material for prototype development and evaluation, and in environments outside of the university that stakeholders provide.

Expected learning outcomes

Knowledge and understanding
After completing the course, the student should be able to:

• (FSR 1) understand, develop and evaluate interactive, intelligent systems who are proactive, reactive, emergent, social and (semi-)autonomous, in collaboration with humans,
• (FSR 2) explain how we in a responsible way can design interactive, intelligent systems for ensuring transparency, accountability, and, adherence to different stakeholders' and the society's (social and cultural) values,
• (FSR 3) understand human-AI interaction as an element of dynamic activity systems based on theories about human activity (including norms and values), and how this translates into computational models useful for human-AI interaction and collaboration.

Competence and skills
After completing the course, the student should be able to:

• (FSR 4) critically analyze applications of theories, methods and tools for developing responsible AI and be able to identify results based on scientifically sound methods,
• (FSR 5) develop and evaluate in collaboration with tentative end users, interactive intelligent systems and environments from the personal, social and ethical perspectives, which include aspects of safety, diversity, equal opportunities and responsible AI,
• (FSR 6) apply relevant theories and models for analyzing, designing and constructing interactive intelligent systems in collaboration with users,
• (FSR 7) collaborate in multi-disciplinary teams.

Judgement and approach
After completing the course, the student should be able to:

• (FSR 8) demonstrate critical thinking, ethical judgment and understanding of the societal needs when developing interactive, intelligent systems,
• (FSR 9) critically evaluate results from users involvment in the design process and compare these with theoretical, technological and societal expectations.

Required Knowledge

At least 90 ECTS, including 60 ECTS Computing Science, or 120 ECTS within a study programme. At least 15 ECTS artificial intelligence, including 7.5 ECTS on the advanced level. Proficiency in English equivalent to the level required for basic eligibility for higher studies.

Form of instruction

The course consists of lectures, project work in computer labs and other environments, and exercises in small groups. Parts of the teaching require mandatory attendance. In addition to scheduled activities the course also requires individual work with the material.

Examination modes

Examination of Module 1 (ELO 1-3 and 8-9) is done through a written examination in halls. The grade on this part is one of the grades Fail (U), Pass (3) or Pass with Merit (4), and Pass with Distinction (5).
 
The examination of Module 2 (ELO 4-7) is done through completing a project conducted in collabortations with an external part, either individually or in group according to instructions provided during the course. The project work is primarly assessing ELO 4-7. Parts of the project work may be done through field studies outside the university in collaboration with societal organisations, and meetings during the project can be located at such organisation. This module contains examining parts that require mandatory attendance such as field studies, examining project meetings and presentation / demonstration of the project. The grade on the practical part is one of the grades Fail (U) or Pass (G).
 

On the whole course one of the grades Fail (U), Pass (3) or Pass with Merit (4), and Pass with Distinction (5) is given. The grade on the course is determined by the grade on module 1.

Adapted examination
The examiner can decide to deviate from the specified forms of examination. Individual adaptation of the examination shall be considered based on the needs of the student. The examination is adapted within the constraints of the expected learning outcomes. A student that needs adapted examination shall no later than 10 days before the examination request adaptation from the Department of Computing Science. The examiner makes a decision of adapted examination and the student is notified.

Other regulations

In a degree, this course may not be included, in whole or in part, simultaneously with another course of similar content. If in doubt, consult the student counselors at the Department of Computer Science and / or program director of programs.

If the syllabus has expired or the course has been discontinued, a student who at some point registered for the course is guaranteed at least three examinations (including the regular examination) according to this syllabus for a maximum period of two years from the syllabus expiring or the course being discontinued.