DARPA formulated the Explainable Artificial Intelligence (XAI) program in 2015 with the goal
to enable end users to better understand, trust, and effectively manage artificially intelligent …

As the performance and complexity of machine learning models have grown significantly
over the last years, there has been an increasing need to develop methodologies to …

Patient care and medical research are changing as a result of the application of artificial
intelligence (AI) in healthcare. To fully realize the potential of AI technology, collaborative …

A new era of medical innovations has begun as a result of the quick adoption of artificial
intelligence (AI) in healthcare, which is changing many aspects of patient care and medical …

Manual verification has become very challenging based on the increasing volume of
information shared online and the role of generative Artificial Intelligence (AI). Thus, AI …

This article discusses the role of machine learning in contact tracing for public health,
particularly in controlling the spread of infectious diseases like COVID-19. The traditional …

The increasing integration of artificial intelligence (ai) in visual analytics (va) tools raises vital
questions about the behavior of users, their trust, and the potential of induced biases when …

DH Despite significant improvements in robot capabilities, they are likely to fail in human-
robot collaborative tasks due to high unpredictability in human environments and varying …

Patient care, diagnosis, and treatment could all be revolutionized by the use of artificial
intelligence (AI) in healthcare. This essay investigates the broad implications of AI in …

Despite significant progress, evaluation of explainable artificial intelligence remains elusive
and challenging. In this paper we propose a fine-grained validation framework that is not …