Survey of explainable AI techniques in healthcare

A Chaddad, J Peng, J Xu, A Bouridane - Sensors, 2023 - mdpi.com
Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …

[HTML][HTML] Natural language processing applied to mental illness detection: a narrative review

T Zhang, AM Schoene, S Ji, S Ananiadou - NPJ digital medicine, 2022 - nature.com
Mental illness is highly prevalent nowadays, constituting a major cause of distress in
people's life with impact on society's health and well-being. Mental illness is a complex multi …

Explainability for large language models: A survey

H Zhao, H Chen, F Yang, N Liu, H Deng, H Cai… - ACM Transactions on …, 2024 - dl.acm.org
Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Combustion machine learning: Principles, progress and prospects

M Ihme, WT Chung, AA Mishra - Progress in Energy and Combustion …, 2022 - Elsevier
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

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 and generalizable graph learning via stochastic attention mechanism

S Miao, M Liu, P Li - International Conference on Machine …, 2022 - proceedings.mlr.press
Interpretable graph learning is in need as many scientific applications depend on learning
models to collect insights from graph-structured data. Previous works mostly focused on …

Explainability in graph neural networks: A taxonomic survey

H Yuan, H Yu, S Gui, S Ji - IEEE transactions on pattern …, 2022 - ieeexplore.ieee.org
Deep learning methods are achieving ever-increasing performance on many artificial
intelligence tasks. A major limitation of deep models is that they are not amenable to …

[HTML][HTML] Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

G Yang, Q Ye, J Xia - Information Fusion, 2022 - Elsevier
Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …

[图书][B] Human-centered AI

B Shneiderman - 2022 - books.google.com
The remarkable progress in algorithms for machine and deep learning have opened the
doors to new opportunities, and some dark possibilities. However, a bright future awaits …

Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems

B Shneiderman - ACM Transactions on Interactive Intelligent Systems …, 2020 - dl.acm.org
This article attempts to bridge the gap between widely discussed ethical principles of Human-
centered AI (HCAI) and practical steps for effective governance. Since HCAI systems are …