With radiology shortages affecting over half of the global population, the potential of artificial
intelligence to revolutionize medical diagnosis and treatment is ever more important …

Advances in AI technologies have resulted in superior levels of AI-based model
performance. However, this has also led to a greater degree of model complexity, resulting …

The growing need for in-depth analysis of predictive models leads to a series of new
methods for explaining their local and global properties. Which of these methods is the best …

Several studies have addressed the importance of context and users' knowledge and
experience in quantifying the usability and effectiveness of the explanations generated by …

In the last decade, there has been increasing interest in allowing users to understand how
the predictions of machine-learned models come about, thus increasing transparency and …

Genetic programming (GP) has the potential to generate explainable results, especially
when used for dimensionality reduction. In this research, we investigate the potential of …

The goal of Explainable AI (XAI) is to design methods to provide insights into the reasoning
process of black-box models, such as deep neural networks, in order to explain them to …

While recently developed NLP explainability methods let us open the black box in various
ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool …

Artificial Intelligence (AI) systems are increasingly used in high-stakes domains of our life,
highlighting the need to explain these decisions and to make sure that they are aligned with …

The emergence of 6G networks heralds a transformative era in network slicing, facilitating
tailored service delivery and optimal resource utilization. Despite its promise, network slice …