Predictions obtained by, eg, artificial neural networks have a high accuracy but humans
often perceive the models as black boxes. Insights about the decision making are mostly …

Despite the highest classification accuracy in wide varieties of application areas, artificial
neural network has one disadvantage. The way this Network comes to a decision is not …

Artificial neural networks (ANN) have been widely used and have achieved remarkable
achievements. However, neural networks with high accuracy and good performance often …

The rapid growth of the number of new mobile malware variants has posed a severe threat
to user's property and privacy. Recent studies show that deep neural networks can detect …

The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …

The need for explanations of ML systems is growing as new models outperform their
predecessors while becoming more complex and less comprehensible for their end-users …

Background: Despite the advancement in eXplainable Artificial Intelligence, the
explanations provided by model-agnostic predictors still call for improvements (ie, lack of …

Explainable artificial intelligence is a very active research topic at the time of writing this
chapter. What is perhaps less appreciated is that the concepts behind this topic have been …

Neural networks have many applications in safety and mission critical systems. As industrial
standards in various safety-critical domains require developers of critical systems to provide …

Artificial neural network classifiers are widespread models used by many machine learning
engineers. Although due to fact they are black box models, in mission critical areas (like …