Evaluating the quality of machine learning explanations: A survey on methods and metrics
The most successful Machine Learning (ML) systems remain complex black boxes to end-
users, and even experts are often unable to understand the rationale behind their decisions …
users, and even experts are often unable to understand the rationale behind their decisions …
Explainable deep learning models in medical image analysis
Deep learning methods have been very effective for a variety of medical diagnostic tasks
and have even outperformed human experts on some of those. However, the black-box …
and have even outperformed human experts on some of those. However, the black-box …
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 …
strategy of decision-making in different life domains. Regardless of this achievement, AI …
Explainable reinforcement learning: A survey
Abstract Explainable Artificial Intelligence (XAI), ie, the development of more transparent and
interpretable AI models, has gained increased traction over the last few years. This is due to …
interpretable AI models, has gained increased traction over the last few years. This is due to …
Explaining the black-box model: A survey of local interpretation methods for deep neural networks
Y Liang, S Li, C Yan, M Li, C Jiang - Neurocomputing, 2021 - Elsevier
Recently, a significant amount of research has been investigated on interpretation of deep
neural networks (DNNs) which are normally processed as black box models. Among the …
neural networks (DNNs) which are normally processed as black box models. Among the …
Robustness gym: Unifying the NLP evaluation landscape
Despite impressive performance on standard benchmarks, deep neural networks are often
brittle when deployed in real-world systems. Consequently, recent research has focused on …
brittle when deployed in real-world systems. Consequently, recent research has focused on …
Xair: A systematic metareview of explainable ai (xai) aligned to the software development process
Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in
regard to its practical implementation in various application domains. To combat the lack of …
regard to its practical implementation in various application domains. To combat the lack of …
On quantitative aspects of model interpretability
Despite the growing body of work in interpretable machine learning, it remains unclear how
to evaluate different explainability methods without resorting to qualitative assessment and …
to evaluate different explainability methods without resorting to qualitative assessment and …
[HTML][HTML] Evaluating the necessity of the multiple metrics for assessing explainable AI: A critical examination
This paper investigates the specific properties of Explainable Artificial Intelligence (xAI),
particularly when implemented in AI/ML models across high-stakes sectors, in this case …
particularly when implemented in AI/ML models across high-stakes sectors, in this case …
Explainable ai for interpretable credit scoring
With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted
enthusiasm in Financial Technology (FinTech), applications such as credit scoring have …
enthusiasm in Financial Technology (FinTech), applications such as credit scoring have …