Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

The automotive industry has witnessed an increasing level of development in the past
decades; from manufacturing manually operated vehicles to manufacturing vehicles with a …

The explanatory capacity of interpretable fuzzy rule-based classifiers is usually limited to
offering explanations for the predicted class only. A lack of potentially useful explanations for …

Classification algorithms have recently acquired great popularity due to their efficiency to
generate models capable of solving high complexity problems. Specifically, black box …

In commentary driving, drivers verbalise their observations, assessments and intentions. By
speaking out their thoughts, both learning and expert drivers are able to create a better …

We introduce a novel framework to deal with fairness, accountability and explainability of
intelligent systems. This framework puts together several tools to deal with bias at the level …

Abstract Machine learning models are widely used in real-world applications. However, their
complexity makes it often challenging to interpret the rationale behind their decisions …

Artificial Intelligence is being applied in a multitude of scenarios that are sensitive to the
human user, ie, medical diagnosis, granting loans, human resources management, among …

EXplainable Artificial Intelligence (XAI) is of in-creasing importance as researchers and
practitioners seek better transparency and verifiability of AI systems. Mamdani fuzzy systems …

Explaining a decision requires some properties that have been studied and established in
cognitive sciences. An important one is the contrastive nature of explanations: an …