As machine learning (ML) models and datasets increase in complexity, the demand for
methods that enhance explainability and interpretability becomes paramount. Prototypes, by …

Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-
markers indicative of the onset and progress of respiratory abnormalities/conditions has …

We propose a framework for generating counterfactual explanations of black-box classifiers,
which answer the question “What has to change for this to be classified as X instead of Y?” …

The use of symbolic knowledge representation and reasoning as a way to resolve the lack of
transparency of machine learning classifiers is a research area that has lately gained a lot of …

Deep learning models have achieved impressive performance in various tasks, but they are
usually opaque with regards to their inner complex operation, obfuscating the reasons for …

The recent spike in certified Artificial Intelligence tools for healthcare has renewed the
debate around adoption of this technology. One thread of such debate concerns Explainable …

Given two datasets, ie, two sets of tuples of constants, representing positive and negative
examples, logical separability is the reasoning task of finding a formula in a certain target …

Gender bias in machine translation (MT) systems poses significant challenges that often
result in the reinforcement of harmful stereotypes. Especially in the labour domain where …

The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the
debate around adoption of this technology. One thread of such debate concerns Explainable …

This thesis undertakes a comprehensive exploration of Explainable Artificial Intelligence
(XAI) by synergizing perspectives from both technical AI research and Science, Technology …