Abstract
Explanations have been subject of study in a variety of fields (e.g. philosophy, psychology and social science), experiencing a new wave of popularity in Artificial Intelligence thanks to the success of machine learning (see DARPA's eXplainable AI). Yet, the events of recent times have shown that the effectiveness of intelligent systems is still limited due to their inability to explain their decisions to human users, hence losing in understandability and trustworthiness. In this talk, I will give an overview of my research, aiming at developing systems able to automatically generate explanations using external background knowledge. In particular, I will show how such systems can be based on the existing research on explanations, combined with AI techniques and the large-scale knowledge sources available nowadays.
Bio
I am a Research Associate in the Knowledge Representation and Reasoning group of the Vrije Universiteit of Amsterdam (NL). My research focuses on creating transparent AI systems that generate explanations through a combination of machine learning, semantic technologies, and knowledge from large, heterogeneous knowledge graphs. As part of my research activities, I am member of the CEUR-WS Editorial Board and the Knowledge Capture conference (K-CAP) Steering Committee, while I have organised workshop series (Recoding Black Mirror, Application of Semantic Web Technologies in Robotics, Linked Data 4 Knowledge Discovery) and Summer Schools (the 2015 and 2016 Semantic Web Summer School).
Twitter: @IlaTiddi
Website : https://kmitd.github.io/ilaria