Interpretability for Machine Learning

Recent important breakthroughs of modern Machine Learning (ML) and Deep Learning methods are accompanied by the concerns about transparency and fairness of ML systems. In this context interpretability, understood as the ability to explain and present ML systems to humans, becomes an important requirement for modern ML systems. This research program will tackle several important aspects of interpretability, including novel use of relational knowledge representation techniques in which I have significant accomplishments. The programs principal axes are the involvement of graduate students, as well as collaborative applications of Machine Learning in Dalhousie Universitys priority research areas of Health and Oceans.