Add to ORKGMachine learning models often exhibit complex behavior that is difficult to understand. Recent research in explainable AI has produced promising techniques to explain the inner workings of such models using feature contribution vectors. These vectors are helpful in a wide variety of applications. However, there are many parameters involved in this process and determining which settings are best is difficult due to the subjective nature of evaluating interpretability.\u3cbr/\u3eTo this end, we introduce ExplainExplore: an interactive explanation system to explore explanations that fit the subjective preference of data scientists. We leverage the domain knowledge of the data scientist to find optimal parameter settings and instance perturbati...
This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence ...
To foster usefulness and accountability of machine learning (ML), it is essential to explain a model...
Machine Learning Explainability: Exploring Automated Decision-Making Through Transparent Modelling a...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communi...
The thesis tackles two problems in the recently-born field of Explainable AI (XAI), and proposes som...
Recent work on interpretability in machine learning and AI has focused on the building of simplified...
Recent work on interpretability in machine learning and AI has focused on the building of simplified...
We take inspiration from the study of human explanation to inform the design and evaluation of inter...
In this review, we examine the problem of designing interpretable and explainable machine learning m...
Research on Machine learning (ML) explainability has received a lot of focus in recent times. The in...
As deep learning methods have obtained tremendous success over the years, our understanding of these...
Modern machine learning methods allow for complex and in-depth analytics, but the predictive models ...
This chapter surveys and analyses visual methods of explainability of Machine Learning (ML) approach...
This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence ...
To foster usefulness and accountability of machine learning (ML), it is essential to explain a model...
Machine Learning Explainability: Exploring Automated Decision-Making Through Transparent Modelling a...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communi...
The thesis tackles two problems in the recently-born field of Explainable AI (XAI), and proposes som...
Recent work on interpretability in machine learning and AI has focused on the building of simplified...
Recent work on interpretability in machine learning and AI has focused on the building of simplified...
We take inspiration from the study of human explanation to inform the design and evaluation of inter...
In this review, we examine the problem of designing interpretable and explainable machine learning m...
Research on Machine learning (ML) explainability has received a lot of focus in recent times. The in...
As deep learning methods have obtained tremendous success over the years, our understanding of these...
Modern machine learning methods allow for complex and in-depth analytics, but the predictive models ...
This chapter surveys and analyses visual methods of explainability of Machine Learning (ML) approach...
This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence ...
To foster usefulness and accountability of machine learning (ML), it is essential to explain a model...
Machine Learning Explainability: Exploring Automated Decision-Making Through Transparent Modelling a...