Interpretability in 
Deep Learning

Artificial intelligence and machine learning approaches are often considered as black boxes, i.e. as a type of algorithms that accomplish learning tasks but cannot explain their knowledge. However, as artificial intelligence is increasingly absorbed as adopted for accomplishing cognitive tasks for human beings, it is becoming important that the artificial intelligence models are understandable by humans, such that artificial and human intelligence can co-exist and collaborate. In critical tasks such as deriving, from given data, a correct medical diagnosis and prognosis, collaboration between artificial and human intelligence in imperative so that the suggestions or decision from artificial intelligence are both more accurate and more trustworthy.

 

This intensive course will consider different topics of importance regarding explainable artificial intelligence, equipping the students with knowledge of approaches that can be used to explain artificial intelligence, and artificial intelligence approaches that are more explainable than others. In addition, the students will receive practical skills of applying selected approaches for explaining artificial intelligence, which will equip the students with practical skills of adapting to the rapid pace of technology development in the field of explainable artificial intelligence.

 

The course lectures are subject to change, based on contemporary developments in the field and emergence of exciting new topics. The currently planned coverage includes, but is not limited to: