[HTML][HTML] Exploring artificial intelligence for applications of drones in forest ecology and management
This paper highlights the significance of Artificial Intelligence (AI) in the realm of drone
applications in forestry. Drones have revolutionized various forest operations, and their role …
applications in forestry. Drones have revolutionized various forest operations, and their role …
[HTML][HTML] On generating trustworthy counterfactual explanations
Deep learning models like chatGPT exemplify AI success but necessitate a deeper
understanding of trust in critical sectors. Trust can be achieved using counterfactual …
understanding of trust in critical sectors. Trust can be achieved using counterfactual …
From 3D point‐cloud data to explainable geometric deep learning: State‐of‐the‐art and future challenges
A Saranti, B Pfeifer, C Gollob… - … : Data Mining and …, 2024 - Wiley Online Library
We present an exciting journey from 3D point‐cloud data (PCD) to the state of the art in
graph neural networks (GNNs) and their evolution with explainable artificial intelligence …
graph neural networks (GNNs) and their evolution with explainable artificial intelligence …
Ironies of artificial intelligence
MR Endsley - Ergonomics, 2023 - Taylor & Francis
Abstract Bainbridge's Ironies of Automation was a prescient description of automation
related challenges for human performance that have characterised much of the 40 years …
related challenges for human performance that have characterised much of the 40 years …
Human-in-the-loop integration with domain-knowledge graphs for explainable federated deep learning
We explore the integration of domain knowledge graphs into Deep Learning for improved
interpretability and explainability using Graph Neural Networks (GNNs). Specifically, a …
interpretability and explainability using Graph Neural Networks (GNNs). Specifically, a …
[HTML][HTML] Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists
The growing field of explainable Artificial Intelligence (xAI) has given rise to a multitude of
techniques and methodologies, yet this expansion has created a growing gap between …
techniques and methodologies, yet this expansion has created a growing gap between …
A practical tutorial on explainable AI techniques
The past years have been characterized by an upsurge in opaque automatic decision
support systems, such as Deep Neural Networks (DNNs). Although DNNs have great …
support systems, such as Deep Neural Networks (DNNs). Although DNNs have great …
An objective metric for Explainable AI: How and why to estimate the degree of explainability
This paper presents a new method for objectively measuring the explainability of textual
information, such as the outputs of Explainable AI (XAI). We introduce a metric called …
information, such as the outputs of Explainable AI (XAI). We introduce a metric called …
Predicting and understanding human action decisions during skillful joint-action using supervised machine learning and explainable-AI
This study investigated the utility of supervised machine learning (SML) and explainable
artificial intelligence (AI) techniques for modeling and understanding human decision …
artificial intelligence (AI) techniques for modeling and understanding human decision …
Efficient approximation of asymmetric shapley values using functional decomposition
Abstract Asymmetric Shapley values (ASVs) are an extension of Shapley values that allow a
user to incorporate partial causal knowledge into the explanation process. Unfortunately …
user to incorporate partial causal knowledge into the explanation process. Unfortunately …