Results 31 to 40 of about 23,034 (235)

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, 2020
Department of Computer Science and EngineeringAs deep learning has grown fast, so did the desire to interpret deep learning black boxes. As a result, many analysis tools have emerged to interpret it.
Lee, Ginkyeng
core  

Enhancing transparency and trust in AI-powered manufacturing: A survey of explainable AI (XAI) applications in smart manufacturing in the era of industry 4.0/5.0

ICT Express
Explainable Artificial Intelligence (XAI) is crucial for the transition from the fourth to fifth industrial revolution, providing transparency and fostering user confidence in Artificial Intelligence (AI) powered systems.
Konstantinos Nikiforidis, Alkiviadis Kyrtsoglou, Thanasis Vafeiadis, Thanasis Kotsiopoulos, Alexandros Nizamis, Dimosthenis Ioannidis, Konstantinos Votis, Dimitrios Tzovaras, Panagiotis Sarigiannidis   +8 more
doaj   +1 more source

XAITK: The explainable AI toolkit

Applied AI Letters, 2021
Recent advances in artificial intelligence (AI), driven mainly by deep neural networks, have yielded remarkable progress in fields, such as computer vision, natural language processing, and reinforcement learning.
Brian Hu, Paul Tunison, Bhavan Vasu, Nitesh Menon, Roddy Collins, Anthony Hoogs   +5 more
doaj   +1 more source

Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

IEEE Access, 2023
Explainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields.
Seunghee Lee, Seonyoung Kim, Jieun Lee, Jong-Yeup Kim, Mi-Hwa Song, Suehyun Lee   +5 more
doaj   +1 more source

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

IEEE Open Journal of the Communications Society, 2022
Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be ...
Senthil Kumar Jagatheesaperumal, Quoc-Viet Pham, Rukhsana Ruby, Zhaohui Yang, Chunmei Xu, Zhaoyang Zhang   +5 more
doaj   +1 more source

Robot Mindreading and the Problem of Trust [PDF]

, 2020
This paper raises three questions regarding the attribution of beliefs, desires, and intentions to robots. The first one is whether humans in fact engage in robot mindreading. If they do, this raises a second question: does robot mindreading foster trust
Páez, Andrés
core   +1 more source

CLinNET: An Interpretable and Uncertainty‐Aware Deep Learning Framework for Multi‐Modal Clinical Genomics

Advanced Science, EarlyView.
Identifying disease‐causing genes in neurocognitive disorders remains challenging due to variants of uncertain significance. CLinNET employs dual‐branch neural networks integrating Reactome pathways and Gene Ontology terms to provide pathway‐level interpretability of genomic alterations.
Ivan Bakhshayeshi, Mohammad Mahdi Hosseini, Ahmadreza Argha, Roxana Zahedi, Nigel H. Lovell, Hamid Alinejad‐Rokny   +5 more
wiley   +1 more source

Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications

Big Data and Cognitive Computing
Artificial intelligence (AI) encompasses the development of systems that perform tasks typically requiring human intelligence, such as reasoning and learning.
Sayda Umma Hamida, Mohammad Jabed Morshed Chowdhury, Narayan Ranjan Chakraborty, Kamanashis Biswas, Shahrab Khan Sami   +4 more
doaj   +1 more source

Applications of Explainable Artificial Intelligence in Diagnosis and Surgery

Diagnostics, 2022
In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult.
Yiming Zhang, Ying Weng, Jonathan Lund
doaj   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source