Explainable Artificial Intelligence (XAI) Techniques - A Review and Case Study
L. Lee Kaijen (TU Delft - Electrical Engineering, Mathematics and Computer Science)
C. Lal – Mentor (TU Delft - Cyber Security)
Mauro Conti – Mentor (TU Delft - Cyber Security)
Joana P. Gonçalves – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
The significant progress of Artificial Intelligence (AI) and Machine Learning (ML) techniques such as Deep Learning (DL) has seen success in their adoption in resolving a variety of problems. However, this success has been accompanied by increasing model complexity resulting in a lack of transparency and trustworthiness. Explainable Artificial Intelligence (XAI) has been proposed as a solution to the need for trustworthy AI/ML systems. A large number of studies about XAI are published in recent years, with a majority discussing the specifics of XAI. Hence this work aims to formalize existing XAI literature from a high-level approach in terms of (1) benefits, (2) requirements, (3) challenges and (4) the underlying building blocks involved. Additionally, this paper presents a case study of XAI within the medical image analysis domain followed by future works and research directions in the field and from a general perspective, all to serve as a foundation and reference point to make the topic more accessible to novices.