Research Topics in AI Ethics

AI ethics is the common reference to a collection of sub-fields in AI developed to respond to the issues of how to manage the moral, personal and societal impact of replacing people tasks and roles with AI powered computing. AI Ethics comprises of four main research sub-disciplines: fair-accountable-transparent AI (FAccT), explainable AI (XAI),responsible AI, and machine ethics (also called artificial morality). 

This course gives an introduction to AI Ethics and a general overview of the state of the art in AI ethics through immersing the students in the research process of an AI ethics topic.  

Knowledge.

• Identify the basic problems studied in explainable AI (XAI), Fairness, Accountability and Transparency (FAccT), Responsible AI and machine ethics.
• Understand the premises of the core moral theories.
• Interpret, explain and extend the need for, and challenges of, AI Ethics.
• Experience the entire process of research in machine ethics from the inception of an idea, analysis of research work, refining a research question, planing and executing group work and reporting on the work in the form of a scientific report.

Skills.

• Appraise the ethical aspects of AI problems.
• Match a specific AI Ethics challenge to its most relevant discipline.

General competence.

• Reading and explaining scientific articles.
• Research project management. Scientific reporting