A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability
Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …
their great ability in modeling graph-structured data, GNNs are vastly used in various …
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 …
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 …
Friend or foe? Teaming between artificial intelligence and workers with variation in experience
As artificial intelligence (AI) applications become more pervasive, it is critical to understand
how knowledge workers with different levels and types of experience can team with AI for …
how knowledge workers with different levels and types of experience can team with AI for …
Insomnia: Towards concept-drift robustness in network intrusion detection
Despite decades of research in network traffic analysis and incredible advances in artificial
intelligence, network intrusion detection systems based on machine learning (ML) have yet …
intelligence, network intrusion detection systems based on machine learning (ML) have yet …
[HTML][HTML] SurvSHAP (t): time-dependent explanations of machine learning survival models
Abstract Machine and deep learning survival models demonstrate similar or even improved
time-to-event prediction capabilities compared to classical statistical learning methods yet …
time-to-event prediction capabilities compared to classical statistical learning methods yet …
Adversarial attacks and defenses in explainable artificial intelligence: A survey
H Baniecki, P Biecek - Information Fusion, 2024 - Elsevier
Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …
and trusting statistical and deep learning models, as well as interpreting their predictions …
Human-in-the-loop reinforcement learning: A survey and position on requirements, challenges, and opportunities
Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …
enable agents to learn and perform tasks autonomously with superhuman performance …
[HTML][HTML] A reusable benchmark of brain-age prediction from M/EEG resting-state signals
Population-level modeling can define quantitative measures of individual aging by applying
machine learning to large volumes of brain images. These measures of brain age, obtained …
machine learning to large volumes of brain images. These measures of brain age, obtained …
An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study
SR Mirjalili, S Soltani, Z Heidari Meybodi… - Cardiovascular …, 2023 - Springer
Background Various predictive models have been developed for predicting the incidence of
coronary heart disease (CHD), but none of them has had optimal predictive value. Although …
coronary heart disease (CHD), but none of them has had optimal predictive value. Although …