The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Process industry encompasses the transformation of individual raw ingredients into final
products. Increasingly, Artificial Intelligence (AI) systems in the industry have led to higher …

In recent years, there has been a rapidly expanding focus on explaining the predictions
made by black-box AI systems that handle image and tabular data. However, considerably …

Multivariate time series are used in many science and engineering domains, including
health-care, astronomy, and high-performance computing. A recent trend is to use machine …

In the healthcare domain, trust, confidence, and functional understanding are critical for
decision support systems, therefore, presenting challenges in the prevalent use of black-box …

Abstract Electric Load Forecasting (ELF) is the central instrument for planning and
controlling demand response programs, electricity trading, and consumption optimization …

In recent years, machine learning methods have been rapidly implemented in the medical
domain. However, current state-of-the-art methods usually produce opaque, black-box …

This paper profiles the recent research work on eXplainable AI (XAI), at the Insight Centre for
Data Analytics. This work concentrates on post-hoc explanation-by-example solutions to XAI …

Time series classification has received great attention over the past decade with a wide
range of methods focusing on predictive performance by exploiting various types of temporal …

In this work, a perturbation-based model-agnostic explanation method for time series
classification is presented. One of the main novelties of the proposed method is that the …