Add to ORKGIn machine learning, there are currently debates about what an explanation or explainable model is and what is necessary for a given purpose. This post details the concepts of explanation and interpretation to help clarify the difference between the two; discusses how, although interpretation is preferable, explanation is the only option for many machine learning techniques; and then details a clustering technique that aids explanation for unsupervised machine learning
Machine learning-based systems are now part of a wide array of real-world applications seamlessly em...
Recent work on interpretability in machine learning and AI has focused on the building of simplified...
Machine Learning (ML) provides important techniques for classification and predictions. Most of thes...
[0001] In machine learning, there are currently debates about what an explanation or explai...
We study the problem of explainability-first clustering where explainability becomes a first-class c...
Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into ...
Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grou...
This paper argues that there are two different types of causes that we can wish to understand when w...
Unsupervised learning is widely recognized as one of the most important challenges facing machine le...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
Machine Learning Explainability: Exploring Automated Decision-Making Through Transparent Modelling a...
This study focuses on exploring the use of local interpretability methods for explaining time series...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
The expanding field of eXplainable Artificial Intelligence research is primarily concerned with the ...
In the last decade, machine learning evolved from a sub-field of computer science into one of the mo...
Machine learning-based systems are now part of a wide array of real-world applications seamlessly em...
Recent work on interpretability in machine learning and AI has focused on the building of simplified...
Machine Learning (ML) provides important techniques for classification and predictions. Most of thes...
[0001] In machine learning, there are currently debates about what an explanation or explai...
We study the problem of explainability-first clustering where explainability becomes a first-class c...
Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into ...
Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grou...
This paper argues that there are two different types of causes that we can wish to understand when w...
Unsupervised learning is widely recognized as one of the most important challenges facing machine le...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
Machine Learning Explainability: Exploring Automated Decision-Making Through Transparent Modelling a...
This study focuses on exploring the use of local interpretability methods for explaining time series...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
The expanding field of eXplainable Artificial Intelligence research is primarily concerned with the ...
In the last decade, machine learning evolved from a sub-field of computer science into one of the mo...
Machine learning-based systems are now part of a wide array of real-world applications seamlessly em...
Recent work on interpretability in machine learning and AI has focused on the building of simplified...
Machine Learning (ML) provides important techniques for classification and predictions. Most of thes...