Human-in-the-loop machine learning: a state of the art
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 …
algorithms generically called human-in-the-loop machine learning. Depending on who is in …
[HTML][HTML] Notions of explainability and evaluation approaches for explainable artificial intelligence
Abstract Explainable Artificial Intelligence (XAI) has experienced a significant growth over
the last few years. This is due to the widespread application of machine learning, particularly …
the last few years. This is due to the widespread application of machine learning, particularly …
[HTML][HTML] Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …
outperforming human performance at certain tasks. There is no doubt that AI is important to …
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …
harnessed appropriately, may deliver the best of expectations over many application sectors …
Recent advances in trustworthy explainable artificial intelligence: Status, challenges, and perspectives
Artificial intelligence (AI) and machine learning (ML) have come a long way from the earlier
days of conceptual theories, to being an integral part of today's technological society. Rapid …
days of conceptual theories, to being an integral part of today's technological society. Rapid …
[HTML][HTML] Explainable, trustworthy, and ethical machine learning for healthcare: A survey
With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …
for critical applications like healthcare, the questions about liability, trust, and interpretability …
[HTML][HTML] Relation between prognostics predictor evaluation metrics and local interpretability SHAP values
Maintenance decisions in domains such as aeronautics are becoming increasingly
dependent on being able to predict the failure of components and systems. When data …
dependent on being able to predict the failure of components and systems. When data …
Classification of explainable artificial intelligence methods through their output formats
Machine and deep learning have proven their utility to generate data-driven models with
high accuracy and precision. However, their non-linear, complex structures are often difficult …
high accuracy and precision. However, their non-linear, complex structures are often difficult …
Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning
Healthcare using body sensor data has been getting huge research attentions by a wide
range of researchers because of its good practical applications such as smart health care …
range of researchers because of its good practical applications such as smart health care …
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage …
Abstract The latest Deep Learning (DL) models for detection and classification have
achieved an unprecedented performance over classical machine learning algorithms …
achieved an unprecedented performance over classical machine learning algorithms …