Explainable ai and reinforcement learning—a systematic review of current approaches and trends
Research into Explainable Artificial Intelligence (XAI) has been increasing in recent years as
a response to the need for increased transparency and trust in AI. This is particularly …
a response to the need for increased transparency and trust in AI. This is particularly …
Model agnostic generation of counterfactual explanations for molecules
GP Wellawatte, A Seshadri, AD White - Chemical science, 2022 - pubs.rsc.org
An outstanding challenge in deep learning in chemistry is its lack of interpretability. The
inability of explaining why a neural network makes a prediction is a major barrier to …
inability of explaining why a neural network makes a prediction is a major barrier to …
Explainable deep learning in plant phenotyping
The increasing human population and variable weather conditions, due to climate change,
pose a threat to the world's food security. To improve global food security, we need to …
pose a threat to the world's food security. To improve global food security, we need to …
Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection
In this paper, we evaluate the performance and analyze the explainability of machine
learning models boosted by feature selection in predicting COVID-19-positive cases from …
learning models boosted by feature selection in predicting COVID-19-positive cases from …
Overview and Perspectives on the Assessment and Mitigation of Cognitive Fatigue in Operational Settings
M Salomone, M Audiffren… - Cyber–Physical–Human …, 2023 - Wiley Online Library
The increase in automation of cyber–physical systems is constantly growing. Although this
tendency is associated with significant benefits, particularly in terms of performance or …
tendency is associated with significant benefits, particularly in terms of performance or …