With the growing industry applications of Artificial Intelligence (AI) systems, pre-trained
models and APIs have emerged and greatly lowered the barrier of building AI-powered …

During a research project in which we developed a machine learning (ML) driven
visualization system for non-ML experts, we reflected on interpretability research in ML …

Interactive machine learning (IML) is a learning process in which a user interacts with a
system to iteratively define and optimise a model. Although recent years have illustrated the …

Current Machine Learning (ML) models can make predictions that are as good as or better
than those made by people. The rapid adoption of this technology puts it at the forefront of …

While anomaly detection stands among the most important and valuable problems across
many scientific domains, anomaly detection research often focuses on AI methods that can …

This paper evaluates No-Code AutoML as a solution for challenges in AI product
prototyping, characterized by unpredictability and inaccessibility to non-experts, and …

Intelligent systems in everyday lives learn about their users to tailor services over time.
However, these systems are often designed with little consideration of user agency on their …

The explosion of data science (DS) in all areas of technology coupled with the rapid growth
of machine learning (ML) techniques in the last decade create novel applications in …

In order to increase productivity, capability, and data exploitation, numerous defense
applications are experiencing an integration of state-of-the-art machine learning and AI into …

From predictive medicine to autonomous driving, advances in Artificial Intelligence (AI)
promise to improve people's lives and improve society. As systems that utilize these …