Machine intelligence is very successful at standard recognition tasks when having high-
quality training data. There is still a significant gap between machine-level pattern …

Artificial intelligence (AI) today is very successful at standard pattern-recognition tasks due to
the availability of large amounts of data and advances in statistical data-driven machine …

Abstract Kandinsky Figures and Kandinsky Patterns are mathematically describable, simple,
self-contained hence controllable synthetic test data sets for the development, validation and …

AI follows the notion of human intelligence which is unfortunately not a clearly defined term.
The most common definition given by cognitive science as mental capability, includes …

Motivated by the Gestalt pattern theory, and the Winograd Challenge for language
understanding, we design synthetic experiments to investigate a deep learning algorithm's …

We discuss a perspective aimed at making black box models more eXplainable, within the
eXplainable AI (XAI) strand of research. We argue that the traditional end-to-end learning …

The proliferation of deep neural networks in various domains has seen an increased need
for the interpretability of these models, especially in scenarios where fairness and trust are …

Ambiguity and diversity in human cognition can be regarded a final frontier in developing
equivalent systems of artificial intelligence. Despite astonishing accomplishments, modern …

Code changes constitute one of the most important features of software evolution. Studying
them can provide insights into the nature of software development and also lead to practical …

We present a new Decision Tree Classifier (DTC) induction algorithm that produces vastly
more interpretable trees in many situations. These understandable trees are highly relevant …