[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 …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging
Explainable artificial intelligence (XAI) has experienced a vast increase in recognition over
the last few years. While the technical developments are manifold, less focus has been …
the last few years. While the technical developments are manifold, less focus has been …
Multimodal explainable artificial intelligence: A comprehensive review of methodological advances and future research directions
N Rodis, C Sardianos, P Radoglou-Grammatikis… - IEEE …, 2024 - ieeexplore.ieee.org
Despite the fact that Artificial Intelligence (AI) has boosted the achievement of remarkable
results across numerous data analysis tasks, however, this is typically accompanied by a …
results across numerous data analysis tasks, however, this is typically accompanied by a …
How interpretable machine learning can benefit process understanding in the geosciences
Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering
new opportunities to improve our understanding of the complex Earth system. IML goes …
new opportunities to improve our understanding of the complex Earth system. IML goes …
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically
approved applications use this technology. Most approaches, however, predict categorical …
approved applications use this technology. Most approaches, however, predict categorical …
Cross-validation strategy impacts the performance and interpretation of machine learning models
Abstract Machine learning algorithms are able to capture complex, nonlinear, interacting
relationships and are increasingly used to predict agricultural yield variability at regional and …
relationships and are increasingly used to predict agricultural yield variability at regional and …
Explainable software systems: from requirements analysis to system evaluation
L Chazette, W Brunotte, T Speith - Requirements Engineering, 2022 - Springer
The growing complexity of software systems and the influence of software-supported
decisions in our society sparked the need for software that is transparent, accountable, and …
decisions in our society sparked the need for software that is transparent, accountable, and …
Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations
Deep Learning has reached human-level performance in several medical tasks including
classification of histopathological images. Continuous effort has been made at finding …
classification of histopathological images. Continuous effort has been made at finding …
Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems
M Kinney, M Anastasiadou, M Naranjo-Zolotov… - Heliyon, 2024 - cell.com
As artificial intelligence systems gain traction, their trustworthiness becomes paramount to
harness their benefits and mitigate risks. This study underscores the pressing need for an …
harness their benefits and mitigate risks. This study underscores the pressing need for an …