Editorial Introduction for the Special Series (Part I) on AI in Signal & Data Science - Toward Explainable, Reliable, and Sustainable Machine Learning

Abstract

Machine learning methods are the backbone of AI and data science. As we embrace the era of deep learning and foundation models, marked by improving performance due to enhanced computing hardware and data scaling, numerous challenges remain. In areas such as healthcare, government decision making and scientific fields, there is a pressing need to develop transparent models that generate interpretable results. Equally important is the reliability of these models, which ensures their robustness and their ability to generalize to new datasets. This is particularly crucial in sectors like healthcare and autonomous driving, where the stakes are high. The recent trend of increasing model sizes also raises concerns about the environmental and societal impacts of training and deploying large models. Therefore, it is essential to mitigate these impacts as much as possible.