Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey

W Ding, M Abdel-Basset, H Hawash, AM Ali - Information Sciences, 2022‏ - Elsevier
The continuous advancement of Artificial Intelligence (AI) has been revolutionizing the
strategy of decision-making in different life domains. Regardless of this achievement, AI …

A survey of visual analytics for explainable artificial intelligence methods

G Alicioglu, B Sun - Computers & Graphics, 2022‏ - Elsevier
Deep learning (DL) models have achieved impressive performance in various domains such
as medicine, finance, and autonomous vehicle systems with advances in computing power …

[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

S Ali, T Abuhmed, S El-Sappagh, K Muhammad… - Information fusion, 2023‏ - Elsevier
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

A multidisciplinary survey and framework for design and evaluation of explainable AI systems

S Mohseni, N Zarei, ED Ragan - ACM Transactions on Interactive …, 2021‏ - dl.acm.org
The need for interpretable and accountable intelligent systems grows along with the
prevalence of artificial intelligence (AI) applications used in everyday life. Explainable AI …

A survey of visual analytics techniques for machine learning

J Yuan, C Chen, W Yang, M Liu… - Computational Visual …, 2021‏ - ieeexplore.ieee.org
Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

Designing a feature selection method based on explainable artificial intelligence

J Zacharias, M von Zahn, J Chen, O Hinz - Electronic Markets, 2022‏ - Springer
Nowadays, artificial intelligence (AI) systems make predictions in numerous high stakes
domains, including credit-risk assessment and medical diagnostics. Consequently, AI …

How do visual explanations foster end users' appropriate trust in machine learning?

F Yang, Z Huang, J Scholtz, DL Arendt - Proceedings of the 25th …, 2020‏ - dl.acm.org
We investigated the effects of example-based explanations for a machine learning classifier
on end users' appropriate trust. We explored the effects of spatial layout and visual …

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models

M Kahng, PY Andrews, A Kalro… - IEEE transactions on …, 2017‏ - ieeexplore.ieee.org
While deep learning models have achieved state-of-the-art accuracies for many prediction
tasks, understanding these models remains a challenge. Despite the recent interest in …

Gamut: A design probe to understand how data scientists understand machine learning models

F Hohman, A Head, R Caruana, R DeLine… - Proceedings of the …, 2019‏ - dl.acm.org
Without good models and the right tools to interpret them, data scientists risk making
decisions based on hidden biases, spurious correlations, and false generalizations. This …

The state of the art in enhancing trust in machine learning models with the use of visualizations

A Chatzimparmpas, RM Martins, I Jusufi… - Computer Graphics …, 2020‏ - Wiley Online Library
Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …