We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to target a model-agnostic distillation approach exemplified with these two frameworks, secondly, to study how these two frameworks interact on a theoretical level, and, thirdly, to investigate use...
This paper provides empirical concerns about post-hoc explanations of black-box ML models, one of th...
A common practice in modern explainable AI is to post-hoc explain black-box machine learning (ML) pr...
Machine learning is currently undergoing an explosion in capability, popularity, and sophistication....
We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by ...
We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by ...
We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by ...
Black box AI systems for automated decision making, often based on machine learning over (big) data,...
The interest in explainable artificial intelligence has grown strongly in recent years because of th...
The rise of sophisticated machine learning models has brought accurate but obscure decision systems,...
This dissertation seeks to clarify and resolve a number of fundamental issues surrounding algorithmi...
Machine learning enables computers to learn from data and fuels artificial intelligence systems with...
In recent years, many accurate decision support systems have been constructed as black boxes, that i...
As the performance and complexity of machine learning models have grown significantly over the last ...
By their nature, the composition of black box models is opaque. This makes the ability to generate e...
The large and still increasing popularity of deep learning clashes with a major limit of neural netw...
This paper provides empirical concerns about post-hoc explanations of black-box ML models, one of th...
A common practice in modern explainable AI is to post-hoc explain black-box machine learning (ML) pr...
Machine learning is currently undergoing an explosion in capability, popularity, and sophistication....
We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by ...
We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by ...
We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by ...
Black box AI systems for automated decision making, often based on machine learning over (big) data,...
The interest in explainable artificial intelligence has grown strongly in recent years because of th...
The rise of sophisticated machine learning models has brought accurate but obscure decision systems,...
This dissertation seeks to clarify and resolve a number of fundamental issues surrounding algorithmi...
Machine learning enables computers to learn from data and fuels artificial intelligence systems with...
In recent years, many accurate decision support systems have been constructed as black boxes, that i...
As the performance and complexity of machine learning models have grown significantly over the last ...
By their nature, the composition of black box models is opaque. This makes the ability to generate e...
The large and still increasing popularity of deep learning clashes with a major limit of neural netw...
This paper provides empirical concerns about post-hoc explanations of black-box ML models, one of th...
A common practice in modern explainable AI is to post-hoc explain black-box machine learning (ML) pr...
Machine learning is currently undergoing an explosion in capability, popularity, and sophistication....
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