Explainable artificial intelligence: a comprehensive review

D Minh, HX Wang, YF Li, TN Nguyen - Artificial Intelligence Review, 2022 - Springer
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 …

From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai

M Nauta, J Trienes, S Pathak, E Nguyen… - ACM Computing …, 2023 - dl.acm.org
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Explainable intrusion detection for cyber defences in the internet of things: Opportunities and solutions

N Moustafa, N Koroniotis, M Keshk… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The field of Explainable Artificial Intelligence (XAI) has garnered considerable research
attention in recent years, aiming to provide interpretability and confidence to the inner …

Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond

X Li, H Xiong, X Li, X Wu, X Zhang, J Liu, J Bian… - … and Information Systems, 2022 - Springer
Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

AB Arrieta, N Díaz-Rodríguez, J Del Ser, A Bennetot… - Information fusion, 2020 - Elsevier
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

Trustworthy AI: From principles to practices

B Li, P Qi, B Liu, S Di, J Liu, J Pei, J Yi… - ACM Computing Surveys, 2023 - dl.acm.org
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

Questioning the AI: informing design practices for explainable AI user experiences

QV Liao, D Gruen, S Miller - Proceedings of the 2020 CHI conference on …, 2020 - dl.acm.org
A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on
the topic. While many recognize the necessity to incorporate explainability features in AI …

Machine learning interpretability: A survey on methods and metrics

DV Carvalho, EM Pereira, JS Cardoso - Electronics, 2019 - mdpi.com
Machine learning systems are becoming increasingly ubiquitous. These systems's adoption
has been expanding, accelerating the shift towards a more algorithmic society, meaning that …

Explainable deep learning: A field guide for the uninitiated

G Ras, N Xie, M Van Gerven, D Doran - Journal of Artificial Intelligence …, 2022 - jair.org
Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions

S Atakishiyev, M Salameh, H Yao, R Goebel - IEEE Access, 2024 - ieeexplore.ieee.org
Autonomous driving has achieved significant milestones in research and development over
the last two decades. There is increasing interest in the field as the deployment of …