Human and Artificial Intelligence Systems - Transfer of Knowledge
Project Description
The dissertation aims to investigate the knowledge transfer between humans and artificial intelligence systems. As machine learning becomes the primary driver of these systems, the need to comprehend and transfer knowledge acquired by machines increases. The main research interest is on approaches, strategies, and methods to render machine-made decisions comprehensible to humans, i.e. "What the machine has learned, such that it makes particular decisions?". Another area of interest that within the scope of this project is the machines' learning phase. This part is approached as the challenge of transferring knowledge into machines, i.e. "What did the machine see during its learning phase, such that it learned particular concepts?". This dissertation draws primarily on researches in three main areas: machine learning, information visualization, and knowledge transfer. They are intended to provide the author with the following know-hows on: getting information in and out from the artificial intelligence, conveying the extracted information to human users, and conceptualizing a sound framework of transferring knowledge, respectively.
