Business analytics aims to enable organizations to make quicker, better, and more
intelligent decisions with the aim to create business value. To date, the major focus in the …

Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

Deep learning has recently seen rapid development and received significant attention due
to its state-of-the-art performance on previously-thought hard problems. However, because …

Without good models and the right tools to interpret them, data scientists risk making
decisions based on hidden biases, spurious correlations, and false generalizations. This …

Research in machine learning has become very popular in recent years, with many types of
models proposed to comprehend and predict patterns and trends in data originating from …

Successful machine learning (ML) applications require iterations on both modeling and the
underlying data. While prior visualization tools for ML primarily focus on modeling, our …

This chapter focuses on describing how the human‐centered design (HCD) process can be
revisited and expanded in an artificial intelligence (AI) context, proposing a methodological …

Research in the social sciences has shown that expectations are an important factor in
explanations as used between humans: rather than explaining the cause of an event per se …

Deployment of modern data-driven machine learning methods, most often realized by deep
neural networks (DNNs), in safety-critical applications such as health care, industrial plant …

Machine learning models are currently being deployed in a variety of real-world applications
where model predictions are used to make decisions about healthcare, bank loans, and …