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 …

[HTML][HTML] Notions of explainability and evaluation approaches for explainable artificial intelligence

G Vilone, L Longo - Information Fusion, 2021 - Elsevier
Abstract Explainable Artificial Intelligence (XAI) has experienced a significant growth over
the last few years. This is due to the widespread application of machine learning, particularly …

[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

S Ali, T Abuhmed, S El-Sappagh, K Muhammad… - Information fusion, 2023 - Elsevier
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

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 …

Representation engineering: A top-down approach to ai transparency

A Zou, L Phan, S Chen, J Campbell, P Guo… - arXiv preprint arXiv …, 2023 - arxiv.org
In this paper, we identify and characterize the emerging area of representation engineering
(RepE), an approach to enhancing the transparency of AI systems that draws on insights …

A survey on neural network interpretability

Y Zhang, P Tiňo, A Leonardis… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Post-hoc interpretability for neural nlp: A survey

A Madsen, S Reddy, S Chandar - ACM Computing Surveys, 2022 - dl.acm.org
Neural networks for NLP are becoming increasingly complex and widespread, and there is a
growing concern if these models are responsible to use. Explaining models helps to address …

Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond

A Hedström, L Weber, D Krakowczyk, D Bareeva… - Journal of Machine …, 2023 - jmlr.org
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 …

Opportunities and challenges in explainable artificial intelligence (xai): A survey

A Das, P Rad - arXiv preprint arXiv:2006.11371, 2020 - arxiv.org
Nowadays, deep neural networks are widely used in mission critical systems such as
healthcare, self-driving vehicles, and military which have direct impact on human lives …

Explainable machine learning in materials science

X Zhong, B Gallagher, S Liu, B Kailkhura… - npj computational …, 2022 - nature.com
Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …