A review of taxonomies of explainable artificial intelligence (XAI) methods

T Speith - Proceedings of the 2022 ACM conference on fairness …, 2022 - dl.acm.org
The recent surge in publications related to explainable artificial intelligence (XAI) has led to
an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this …

Explainable artificial intelligence applications in cyber security: State-of-the-art in research

Z Zhang, H Al Hamadi, E Damiani, CY Yeun… - IEEE …, 2022 - ieeexplore.ieee.org
This survey presents a comprehensive review of current literature on Explainable Artificial
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …

[HTML][HTML] Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

L Longo, M Brcic, F Cabitza, J Choi, R Confalonieri… - Information …, 2024 - Elsevier
Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

What do we want from Explainable Artificial Intelligence (XAI)?–A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

M Langer, D Oster, T Speith, H Hermanns, L Kästner… - Artificial Intelligence, 2021 - Elsevier
Abstract Previous research in Explainable Artificial Intelligence (XAI) suggests that a main
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …

A multidisciplinary survey and framework for design and evaluation of explainable AI systems

S Mohseni, N Zarei, ED Ragan - ACM Transactions on Interactive …, 2021 - dl.acm.org
The need for interpretable and accountable intelligent systems grows along with the
prevalence of artificial intelligence (AI) applications used in everyday life. Explainable AI …

Tackling prediction uncertainty in machine learning for healthcare

M Chua, D Kim, J Choi, NG Lee… - Nature Biomedical …, 2023 - nature.com
Predictive machine-learning systems often do not convey the degree of confidence in the
correctness of their outputs. To prevent unsafe prediction failures from machine-learning …

[HTML][HTML] A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences

M Graziani, L Dutkiewicz, D Calvaresi… - Artificial intelligence …, 2023 - Springer
Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many
technology products and their fields of application. Machine learning, as a major part of the …

Teach me to explain: A review of datasets for explainable natural language processing

S Wiegreffe, A Marasović - arXiv preprint arXiv:2102.12060, 2021 - arxiv.org
Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated textual
explanations. These explanations are used downstream in three ways: as data …

[HTML][HTML] A review of interpretable ML in healthcare: taxonomy, applications, challenges, and future directions

TAA Abdullah, MSM Zahid, W Ali - Symmetry, 2021 - mdpi.com
We have witnessed the impact of ML in disease diagnosis, image recognition and
classification, and many more related fields. Healthcare is a sensitive field related to …

[HTML][HTML] Social media hate speech detection using explainable artificial intelligence (XAI)

H Mehta, K Passi - Algorithms, 2022 - mdpi.com
Explainable artificial intelligence (XAI) characteristics have flexible and multifaceted
potential in hate speech detection by deep learning models. Interpreting and explaining …