AI and Society: Legal, Ethical and Societal Aspects of AI
AI och samhälle: juridiska, etiska och samhällsrelaterade aspekter av AI

EDA070F, 3 credits

Valid from: Autumn 2020
Decided by: Professor Thomas Johansson
Date of establishment: 2020-12-10

General Information

Division: Computer Science (LTH)
Course type: Third-cycle course
Teaching language: English

Aim

The course aims to give insight into the following three main topics relevant to researchers in technical areas, in particular AI: Responsible Design of Intelligent Systems: understand Responsible AI as part of complex socio-technical systems, Ethical Machines and Human-Agent Interaction.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• - know the terms, concepts and expressions used in the presented material,
• - be able to understand and explain the presented concepts,
• - be able to use the presented concepts to analyse a given problem with them

Competences and Skills

For a passing grade the doctoral student must

• - be able to reason about given questions with the tools presented in the course, at least to a certain level of complexity
• - decide upon the applicability of a certain tool in a given context outside the scope of the course
• - transfer the learned concepts and tools to problems outside the scope of the course where applicable

Judgement and Approach

For a passing grade the doctoral student must - be able to judge a (research related) question with the concepts and analytic / argumentative tools discussed in the context of the course

Course Contents

The content of the course include: ethics in practice – ethical issues in different applications, ethics by design – computational reasoning models for ethical deliberation, explainable AI – mathematical principles and computational approaches, and analysis and modelling of interaction constraints and norms.

Course Literature

Announced prior to course start / through the course home page

Instruction Details

Types of instruction: Lectures, miscellaneous. The course is given in a two-day block format with varying session formats (lectures, discussion groups,...)

Examination Details

Examination format: Written assignments
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: Specific assumed prior knowledge is communicated through the course website, which is available well in advance. Generally the course requires at least some background in Computer Science, Mathematics, Engineering Physics, Electrical Engineering or a closely related topic.

Further Information

Contact the course responsible person / Department representative for information about course instances. The course is given as part of the WASP graduate school and has specific admission rules.

Course Occasion Information

Contact and Other Information

Course coordinator: Elin A. Topp <elin_a.topp@cs.lth.se>
Web page: https://wasp-sweden.org/graduate-school/ai-graduate-school-courses/