Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas

A Adel - Journal of Cloud Computing, 2022 - Springer
Industry 4.0 has been provided for the last 10 years to benefit the industry and the
shortcomings; finally, the time for industry 5.0 has arrived. Smart factories are increasing the …

Re-thinking data strategy and integration for artificial intelligence: concepts, opportunities, and challenges

A Aldoseri, KN Al-Khalifa, AM Hamouda - Applied Sciences, 2023 - mdpi.com
The use of artificial intelligence (AI) is becoming more prevalent across industries such as
healthcare, finance, and transportation. Artificial intelligence is based on the analysis of …

[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 …

Human-in-the-loop machine learning: a state of the art

E Mosqueira-Rey, E Hernández-Pereira… - Artificial Intelligence …, 2023 - Springer
Researchers are defining new types of interactions between humans and machine learning
algorithms generically called human-in-the-loop machine learning. Depending on who is in …

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 …

Counterfactual explanations and how to find them: literature review and benchmarking

R Guidotti - Data Mining and Knowledge Discovery, 2024 - Springer
Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …

Interpreting black-box models: a review on explainable artificial intelligence

V Hassija, V Chamola, A Mahapatra, A Singal… - Cognitive …, 2024 - Springer
Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based
methodological development in a broad range of domains. In this rapidly evolving field …

Trustworthy artificial intelligence: a review

D Kaur, S Uslu, KJ Rittichier, A Durresi - ACM computing surveys (CSUR …, 2022 - dl.acm.org
Artificial intelligence (AI) and algorithmic decision making are having a profound impact on
our daily lives. These systems are vastly used in different high-stakes applications like …

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] How can we manage biases in artificial intelligence systems–A systematic literature review

PS Varsha - International Journal of Information Management Data …, 2023 - Elsevier
Artificial intelligence is similar to human intelligence, and robots in organisations always
perform human tasks. However, AI encounters a variety of biases during its operational …