Human-in-the-loop reinforcement learning: A survey and position on requirements, challenges, and opportunities
Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …
enable agents to learn and perform tasks autonomously with superhuman performance …
From industry 5.0 to forestry 5.0: Bridging the gap with human-centered artificial intelligence
Abstract Purpose of the Review Recent technological innovations in Artificial Intelligence
(AI) have successfully revolutionized many industrial processes, enhancing productivity and …
(AI) have successfully revolutionized many industrial processes, enhancing productivity and …
[HTML][HTML] Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
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 …
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
Impact of generative artificial intelligence models on the performance of citizen data scientists in retail firms
Abstract Generative Artificial Intelligence (AI) models serve as powerful tools for
organizations aiming to integrate advanced data analysis and automation into their …
organizations aiming to integrate advanced data analysis and automation into their …
Explainability pitfalls: Beyond dark patterns in explainable AI
To make explainable artificial intelligence (XAI) systems trustworthy, understanding harmful
effects is important. In this paper, we address an important yet unarticulated type of negative …
effects is important. In this paper, we address an important yet unarticulated type of negative …
Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning
Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …
processes often remain opaque, earning them the characterization of “black-box” models …
[HTML][HTML] CLARUS: An interactive explainable AI platform for manual counterfactuals in graph neural networks
Background: Lack of trust in artificial intelligence (AI) models in medicine is still the key
blockage for the use of AI in clinical decision support systems (CDSS). Although AI models …
blockage for the use of AI in clinical decision support systems (CDSS). Although AI models …
Trustworthy multi-phase liver tumor segmentation via evidence-based uncertainty
C Hu, T **a, Y Cui, Q Zou, Y Wang, W **ao, S Ju… - … Applications of Artificial …, 2024 - Elsevier
Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the
complementary multi-phase information for liver tumor segmentation (LiTS), which are …
complementary multi-phase information for liver tumor segmentation (LiTS), which are …
Transformer models in biomedicine
S Madan, M Lentzen, J Brandt, D Rueckert… - BMC Medical Informatics …, 2024 - Springer
Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence
(AI) field. The transformer model is a type of DNN that was originally used for the natural …
(AI) field. The transformer model is a type of DNN that was originally used for the natural …
Sensors for digital transformation in smart forestry
Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance
forest management while minimizing the environmental impact. The efficacy of AI in this …
forest management while minimizing the environmental impact. The efficacy of AI in this …