Explainable Artificial Intelligence (XAI) Techniques - A Review and Case Study

Explainable Artificial Intelligence (XAI) Techniques - A Review and Case Study

Lee Kaijen, Kaijen (TU Delft Electrical Engineering, Mathematics and Computer Science)

Lal, C. (mentor)
Conti, M. (mentor)
P. Gonçalves, Joana (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-24

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.

XAI
Explainable Artificial Intelligence
Medical Image Analysis
review

http://resolver.tudelft.nl/uuid:3c283c1c-d2bd-4062-845a-490efc8ff4d4

Student theses

bachelor thesis

© 2022 Kaijen Lee Kaijen