Explainable artificial intelligence: a comprehensive review
Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …
State of the art of visual analytics for explainable deep learning
The use and creation of machine‐learning‐based solutions to solve problems or reduce
their computational costs are becoming increasingly widespread in many domains. Deep …
their computational costs are becoming increasingly widespread in many domains. Deep …
[HTML][HTML] Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies
In this paper, we describe a post-hoc explanation-by-example approach to eXplainable AI
(XAI), where a black-box, deep learning system is explained by reference to a more …
(XAI), where a black-box, deep learning system is explained by reference to a more …
Good counterfactuals and where to find them: A case-based technique for generating counterfactuals for explainable AI (XAI)
Recently, a groundswell of research has identified the use of counterfactual explanations as
a potentially significant solution to the Explainable AI (XAI) problem. It is argued that (i) …
a potentially significant solution to the Explainable AI (XAI) problem. It is argued that (i) …
On generating plausible counterfactual and semi-factual explanations for deep learning
There is a growing concern that the recent progress made in AI, especially regarding the
predictive competence of deep learning models, will be undermined by a failure to properly …
predictive competence of deep learning models, will be undermined by a failure to properly …
Instance-based counterfactual explanations for time series classification
In recent years, there has been a rapidly expanding focus on explaining the predictions
made by black-box AI systems that handle image and tabular data. However, considerably …
made by black-box AI systems that handle image and tabular data. However, considerably …
Two-stage deep learning model for Alzheimer's disease detection and prediction of the mild cognitive impairment time
Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
Xair: A framework of explainable ai in augmented reality
Explainable AI (XAI) has established itself as an important component of AI-driven
interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives …
interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives …
[HTML][HTML] Introduction to digital pathology and computer-aided pathology
Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for
many pathologists to understand the engineering and mathematics concepts involved in DP …
many pathologists to understand the engineering and mathematics concepts involved in DP …
Towards interpretable deep reinforcement learning with human-friendly prototypes
Despite recent success of deep learning models in research settings, their application in
sensitive domains remains limited because of their opaque decision-making processes …
sensitive domains remains limited because of their opaque decision-making processes …