Explaining deep neural networks and beyond: A review of methods and applications
With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …
XAI systems evaluation: A review of human and computer-centred methods
The lack of transparency of powerful Machine Learning systems paired with their growth in
popularity over the last decade led to the emergence of the eXplainable Artificial Intelligence …
popularity over the last decade led to the emergence of the eXplainable Artificial Intelligence …
From attribution maps to human-understandable explanations through concept relevance propagation
The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …
powerful but opaque deep learning models. While local XAI methods explain individual …
Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond
The evaluation of explanation methods is a research topic that has not yet been explored
deeply, however, since explainability is supposed to strengthen trust in artificial intelligence …
deeply, however, since explainability is supposed to strengthen trust in artificial intelligence …
Explainable AI methods-a brief overview
Abstract Explainable Artificial Intelligence (xAI) is an established field with a vibrant
community that has developed a variety of very successful approaches to explain and …
community that has developed a variety of very successful approaches to explain and …
Debugging tests for model explanations
We investigate whether post-hoc model explanations are effective for diagnosing model
errors--model debugging. In response to the challenge of explaining a model's prediction, a …
errors--model debugging. In response to the challenge of explaining a model's prediction, a …
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience
A Mamalakis, EA Barnes… - Artificial Intelligence for …, 2022 - journals.ametsoc.org
Convolutional neural networks (CNNs) have recently attracted great attention in geoscience
because of their ability to capture nonlinear system behavior and extract predictive …
because of their ability to capture nonlinear system behavior and extract predictive …
A survey on the interpretability of deep learning in medical diagnosis
Q Teng, Z Liu, Y Song, K Han, Y Lu - Multimedia Systems, 2022 - Springer
Deep learning has demonstrated remarkable performance in the medical domain, with
accuracy that rivals or even exceeds that of human experts. However, it has a significant …
accuracy that rivals or even exceeds that of human experts. However, it has a significant …
Understanding the (extra-) ordinary: Validating deep model decisions with prototypical concept-based explanations
Ensuring both transparency and safety is critical when deploying Deep Neural Networks
(DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has …
(DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has …
[HTML][HTML] CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations
The rise of deep learning in today's applications entailed an increasing need in explaining
the model's decisions beyond prediction performances in order to foster trust and …
the model's decisions beyond prediction performances in order to foster trust and …