Research Area:  Artificial Intelligence

Abstract:

In the last decade, with availability of large datasets and more computing power, machine learning systems have achieved (super)human performance in a wide variety of tasks. Examples of this rapid development can be seen in image recognition, speech analysis, strategic game planning and many more. The problem with many state-of-the-art models is a lack of transparency and interpretability. The lack of thereof is a major drawback in many applications, e.g. healthcare and finance, where rationale for models decision is a requirement for trust. In the light of these issues, explainable artificial intelligence (XAI) has become an area of interest in research community. This paper summarizes recent developments in XAI in supervised learning, starts a discussion on its connection with artificial general intelligence, and gives proposals for further research directions.

Keywords:  

Author(s) Name:  Filip Karlo Dosilovic; Mario Brcic; Nikica Hlupic

Publisher name:  IEEE

DOI:  10.23919/MIPRO.2018.8400040

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/document/8400040